{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# Fully-Connected Neural Nets\n",
    "In the previous homework you implemented a fully-connected two-layer neural network on CIFAR-10. The implementation was simple but not very modular since the loss and gradient were computed in a single monolithic function. This is manageable for a simple two-layer network, but would become impractical as we move to bigger models. Ideally we want to build networks using a more modular design so that we can implement different layer types in isolation and then snap them together into models with different architectures."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "source": [
    "In this exercise we will implement fully-connected networks using a more modular approach. For each layer we will implement a `forward` and a `backward` function. The `forward` function will receive inputs, weights, and other parameters and will return both an output and a `cache` object storing data needed for the backward pass, like this:\n",
    "\n",
    "```python\n",
    "def layer_forward(x, w):\n",
    "  \"\"\" Receive inputs x and weights w \"\"\"\n",
    "  # Do some computations ...\n",
    "  z = # ... some intermediate value\n",
    "  # Do some more computations ...\n",
    "  out = # the output\n",
    "   \n",
    "  cache = (x, w, z, out) # Values we need to compute gradients\n",
    "   \n",
    "  return out, cache\n",
    "```\n",
    "\n",
    "The backward pass will receive upstream derivatives and the `cache` object, and will return gradients with respect to the inputs and weights, like this:\n",
    "\n",
    "```python\n",
    "def layer_backward(dout, cache):\n",
    "  \"\"\"\n",
    "  Receive dout (derivative of loss with respect to outputs) and cache,\n",
    "  and compute derivative with respect to inputs.\n",
    "  \"\"\"\n",
    "  # Unpack cache values\n",
    "  x, w, z, out = cache\n",
    "  \n",
    "  # Use values in cache to compute derivatives\n",
    "  dx = # Derivative of loss with respect to x\n",
    "  dw = # Derivative of loss with respect to w\n",
    "  \n",
    "  return dx, dw\n",
    "```\n",
    "\n",
    "After implementing a bunch of layers this way, we will be able to easily combine them to build classifiers with different architectures.\n",
    "\n",
    "In addition to implementing fully-connected networks of arbitrary depth, we will also explore different update rules for optimization, and introduce Dropout as a regularizer and Batch/Layer Normalization as a tool to more efficiently optimize deep networks.\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=========== You can safely ignore the message below if you are NOT working on ConvolutionalNetworks.ipynb ===========\n",
      "\tYou will need to compile a Cython extension for a portion of this assignment.\n",
      "\tThe instructions to do this will be given in a section of the notebook below.\n",
      "\tThere will be an option for Colab users and another for Jupyter (local) users.\n"
     ]
    }
   ],
   "source": [
    "# As usual, a bit of setup\n",
    "from __future__ import print_function\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.fc_net import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "  \"\"\" returns relative error \"\"\"\n",
    "  return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('X_train: ', (49000, 3, 32, 32))\n",
      "('y_train: ', (49000,))\n",
      "('X_val: ', (1000, 3, 32, 32))\n",
      "('y_val: ', (1000,))\n",
      "('X_test: ', (1000, 3, 32, 32))\n",
      "('y_test: ', (1000,))\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in list(data.items()):\n",
    "  print(('%s: ' % k, v.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Affine layer: forward\n",
    "Open the file `cs231n/layers.py` and implement the `affine_forward` function.\n",
    "\n",
    "Once you are done you can test your implementaion by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.49834967 1.70660132 1.91485297]\n",
      " [3.25553199 3.5141327  3.77273342]]\n",
      "Testing affine_forward function:\n",
      "difference:  9.769847728806635e-10\n"
     ]
    }
   ],
   "source": [
    "# Test the affine_forward function\n",
    "\n",
    "num_inputs = 2\n",
    "input_shape = (4, 5, 6)\n",
    "output_dim = 3\n",
    "\n",
    "input_size = num_inputs * np.prod(input_shape)\n",
    "weight_size = output_dim * np.prod(input_shape)\n",
    "\n",
    "x = np.linspace(-0.1, 0.5, num=input_size).reshape(num_inputs, *input_shape)\n",
    "w = np.linspace(-0.2, 0.3, num=weight_size).reshape(np.prod(input_shape), output_dim)\n",
    "b = np.linspace(-0.3, 0.1, num=output_dim)\n",
    "\n",
    "out, _ = affine_forward(x, w, b)\n",
    "correct_out = np.array([[ 1.49834967,  1.70660132,  1.91485297],\n",
    "                      [ 3.25553199,  3.5141327,   3.77273342]])\n",
    "\n",
    "print(out)\n",
    "# Compare your output with ours. The error should be around e-9 or less.\n",
    "print('Testing affine_forward function:')\n",
    "print('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Affine layer: backward\n",
    "Now implement the `affine_backward` function and test your implementation using numeric gradient checking."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_backward function:\n",
      "dx error:  5.399100368651805e-11\n",
      "dw error:  9.904211865398145e-11\n",
      "db error:  2.4122867568119087e-11\n"
     ]
    }
   ],
   "source": [
    "# Test the affine_backward function\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(10, 2, 3)\n",
    "w = np.random.randn(6, 5)\n",
    "b = np.random.randn(5)\n",
    "dout = np.random.randn(10, 5)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: affine_forward(x, w, b)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: affine_forward(x, w, b)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: affine_forward(x, w, b)[0], b, dout)\n",
    "\n",
    "_, cache = affine_forward(x, w, b)\n",
    "dx, dw, db = affine_backward(dout, cache)\n",
    "\n",
    "# The error should be around e-10 or less\n",
    "print('Testing affine_backward function:')\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dw error: ', rel_error(dw_num, dw))\n",
    "print('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ReLU activation: forward\n",
    "Implement the forward pass for the ReLU activation function in the `relu_forward` function and test your implementation using the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing relu_forward function:\n",
      "difference:  4.999999798022158e-08\n"
     ]
    }
   ],
   "source": [
    "# Test the relu_forward function\n",
    "\n",
    "x = np.linspace(-0.5, 0.5, num=12).reshape(3, 4)\n",
    "\n",
    "out, _ = relu_forward(x)\n",
    "correct_out = np.array([[ 0.,          0.,          0.,          0.,        ],\n",
    "                        [ 0.,          0.,          0.04545455,  0.13636364,],\n",
    "                        [ 0.22727273,  0.31818182,  0.40909091,  0.5,       ]])\n",
    "\n",
    "# Compare your output with ours. The error should be on the order of e-8\n",
    "print('Testing relu_forward function:')\n",
    "print('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ReLU activation: backward\n",
    "Now implement the backward pass for the ReLU activation function in the `relu_backward` function and test your implementation using numeric gradient checking:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing relu_backward function:\n",
      "dx error:  3.2756349136310288e-12\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "x = np.random.randn(10, 10)\n",
    "dout = np.random.randn(*x.shape)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: relu_forward(x)[0], x, dout)\n",
    "\n",
    "_, cache = relu_forward(x)\n",
    "dx = relu_backward(dout, cache)\n",
    "\n",
    "# The error should be on the order of e-12\n",
    "print('Testing relu_backward function:')\n",
    "print('dx error: ', rel_error(dx_num, dx))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## Inline Question 1: \n",
    "\n",
    "We've only asked you to implement ReLU, but there are a number of different activation functions that one could use in neural networks, each with its pros and cons. In particular, an issue commonly seen with activation functions is getting zero (or close to zero) gradient flow during backpropagation. Which of the following activation functions have this problem? If you consider these functions in the one dimensional case, what types of input would lead to this behaviour?\n",
    "1. Sigmoid\n",
    "2. ReLU\n",
    "3. Leaky ReLU\n",
    "\n",
    "## Answer:\n",
    "ReLU\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# \"Sandwich\" layers\n",
    "There are some common patterns of layers that are frequently used in neural nets. For example, affine layers are frequently followed by a ReLU nonlinearity. To make these common patterns easy, we define several convenience layers in the file `cs231n/layer_utils.py`.\n",
    "\n",
    "For now take a look at the `affine_relu_forward` and `affine_relu_backward` functions, and run the following to numerically gradient check the backward pass:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_relu_forward and affine_relu_backward:\n",
      "dx error:  6.750562121603446e-11\n",
      "dw error:  8.162015570444288e-11\n",
      "db error:  7.826724021458994e-12\n"
     ]
    }
   ],
   "source": [
    "from cs231n.layer_utils import affine_relu_forward, affine_relu_backward\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(2, 3, 4)\n",
    "w = np.random.randn(12, 10)\n",
    "b = np.random.randn(10)\n",
    "dout = np.random.randn(2, 10)\n",
    "\n",
    "out, cache = affine_relu_forward(x, w, b)\n",
    "dx, dw, db = affine_relu_backward(dout, cache)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: affine_relu_forward(x, w, b)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: affine_relu_forward(x, w, b)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: affine_relu_forward(x, w, b)[0], b, dout)\n",
    "\n",
    "# Relative error should be around e-10 or less\n",
    "print('Testing affine_relu_forward and affine_relu_backward:')\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dw error: ', rel_error(dw_num, dw))\n",
    "print('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loss layers: Softmax and SVM\n",
    "You implemented these loss functions in the last assignment, so we'll give them to you for free here. You should still make sure you understand how they work by looking at the implementations in `cs231n/layers.py`.\n",
    "\n",
    "You can make sure that the implementations are correct by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing svm_loss:\n",
      "loss:  8.999602749096233\n",
      "dx error:  1.4021566006651672e-09\n",
      "\n",
      "Testing softmax_loss:\n",
      "loss:  2.302545844500738\n",
      "dx error:  9.384673161989355e-09\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "num_classes, num_inputs = 10, 50\n",
    "x = 0.001 * np.random.randn(num_inputs, num_classes)\n",
    "y = np.random.randint(num_classes, size=num_inputs)\n",
    "\n",
    "dx_num = eval_numerical_gradient(lambda x: svm_loss(x, y)[0], x, verbose=False)\n",
    "loss, dx = svm_loss(x, y)\n",
    "\n",
    "# Test svm_loss function. Loss should be around 9 and dx error should be around the order of e-9\n",
    "print('Testing svm_loss:')\n",
    "print('loss: ', loss)\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "\n",
    "dx_num = eval_numerical_gradient(lambda x: softmax_loss(x, y)[0], x, verbose=False)\n",
    "loss, dx = softmax_loss(x, y)\n",
    "\n",
    "# Test softmax_loss function. Loss should be close to 2.3 and dx error should be around e-8\n",
    "print('\\nTesting softmax_loss:')\n",
    "print('loss: ', loss)\n",
    "print('dx error: ', rel_error(dx_num, dx))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Two-layer network\n",
    "In the previous assignment you implemented a two-layer neural network in a single monolithic class. Now that you have implemented modular versions of the necessary layers, you will reimplement the two layer network using these modular implementations.\n",
    "\n",
    "Open the file `cs231n/classifiers/fc_net.py` and complete the implementation of the `TwoLayerNet` class. This class will serve as a model for the other networks you will implement in this assignment, so read through it to make sure you understand the API. You can run the cell below to test your implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H, C = 3, 5, 50, 7\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=N)\n",
    "\n",
    "std = 1e-3\n",
    "model = TwoLayerNet(input_dim=D, hidden_dim=H, num_classes=C, weight_scale=std)\n",
    "\n",
    "print('Testing initialization ... ')\n",
    "W1_std = abs(model.params['W1'].std() - std)\n",
    "b1 = model.params['b1']\n",
    "W2_std = abs(model.params['W2'].std() - std)\n",
    "b2 = model.params['b2']\n",
    "assert W1_std < std / 10, 'First layer weights do not seem right'\n",
    "assert np.all(b1 == 0), 'First layer biases do not seem right'\n",
    "assert W2_std < std / 10, 'Second layer weights do not seem right'\n",
    "assert np.all(b2 == 0), 'Second layer biases do not seem right'\n",
    "\n",
    "print('Testing test-time forward pass ... ')\n",
    "model.params['W1'] = np.linspace(-0.7, 0.3, num=D*H).reshape(D, H)\n",
    "model.params['b1'] = np.linspace(-0.1, 0.9, num=H)\n",
    "model.params['W2'] = np.linspace(-0.3, 0.4, num=H*C).reshape(H, C)\n",
    "model.params['b2'] = np.linspace(-0.9, 0.1, num=C)\n",
    "X = np.linspace(-5.5, 4.5, num=N*D).reshape(D, N).T\n",
    "scores = model.loss(X)\n",
    "correct_scores = np.asarray(\n",
    "  [[11.53165108,  12.2917344,   13.05181771,  13.81190102,  14.57198434, 15.33206765,  16.09215096],\n",
    "   [12.05769098,  12.74614105,  13.43459113,  14.1230412,   14.81149128, 15.49994135,  16.18839143],\n",
    "   [12.58373087,  13.20054771,  13.81736455,  14.43418138,  15.05099822, 15.66781506,  16.2846319 ]])\n",
    "scores_diff = np.abs(scores - correct_scores).sum()\n",
    "assert scores_diff < 1e-6, 'Problem with test-time forward pass'\n",
    "\n",
    "print('Testing training loss (no regularization)')\n",
    "y = np.asarray([0, 5, 1])\n",
    "loss, grads = model.loss(X, y)\n",
    "correct_loss = 3.4702243556\n",
    "assert abs(loss - correct_loss) < 1e-10, 'Problem with training-time loss'\n",
    "\n",
    "model.reg = 1.0\n",
    "loss, grads = model.loss(X, y)\n",
    "correct_loss = 26.5948426952\n",
    "assert abs(loss - correct_loss) < 1e-10, 'Problem with regularization loss'\n",
    "\n",
    "# Errors should be around e-7 or less\n",
    "for reg in [0.0, 0.7]:\n",
    "  print('Running numeric gradient check with reg = ', reg)\n",
    "  model.reg = reg\n",
    "  loss, grads = model.loss(X, y)\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False)\n",
    "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Solver\n",
    "In the previous assignment, the logic for training models was coupled to the models themselves. Following a more modular design, for this assignment we have split the logic for training models into a separate class.\n",
    "\n",
    "Open the file `cs231n/solver.py` and read through it to familiarize yourself with the API. After doing so, use a `Solver` instance to train a `TwoLayerNet` that achieves at least `50%` accuracy on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "tln_solver_accuracy"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 4900) loss: 2.302655\n",
      "(Epoch 0 / 10) train acc: 0.148000; val_acc: 0.146000\n",
      "(Iteration 101 / 4900) loss: 1.794176\n",
      "(Iteration 201 / 4900) loss: 1.693589\n",
      "(Iteration 301 / 4900) loss: 1.604447\n",
      "(Iteration 401 / 4900) loss: 1.734053\n",
      "(Epoch 1 / 10) train acc: 0.475000; val_acc: 0.440000\n",
      "(Iteration 501 / 4900) loss: 1.581779\n",
      "(Iteration 601 / 4900) loss: 1.435525\n",
      "(Iteration 701 / 4900) loss: 1.551642\n",
      "(Iteration 801 / 4900) loss: 1.398511\n",
      "(Iteration 901 / 4900) loss: 1.474098\n",
      "(Epoch 2 / 10) train acc: 0.486000; val_acc: 0.462000\n",
      "(Iteration 1001 / 4900) loss: 1.446392\n",
      "(Iteration 1101 / 4900) loss: 1.555698\n",
      "(Iteration 1201 / 4900) loss: 1.727462\n",
      "(Iteration 1301 / 4900) loss: 1.092570\n",
      "(Iteration 1401 / 4900) loss: 1.403769\n",
      "(Epoch 3 / 10) train acc: 0.514000; val_acc: 0.489000\n",
      "(Iteration 1501 / 4900) loss: 1.496407\n",
      "(Iteration 1601 / 4900) loss: 1.213782\n",
      "(Iteration 1701 / 4900) loss: 1.339614\n",
      "(Iteration 1801 / 4900) loss: 1.374843\n",
      "(Iteration 1901 / 4900) loss: 1.467407\n",
      "(Epoch 4 / 10) train acc: 0.507000; val_acc: 0.494000\n",
      "(Iteration 2001 / 4900) loss: 1.245198\n",
      "(Iteration 2101 / 4900) loss: 1.262008\n",
      "(Iteration 2201 / 4900) loss: 1.213907\n",
      "(Iteration 2301 / 4900) loss: 1.399751\n",
      "(Iteration 2401 / 4900) loss: 1.300017\n",
      "(Epoch 5 / 10) train acc: 0.522000; val_acc: 0.497000\n",
      "(Iteration 2501 / 4900) loss: 1.513907\n",
      "(Iteration 2601 / 4900) loss: 1.025871\n",
      "(Iteration 2701 / 4900) loss: 1.344213\n",
      "(Iteration 2801 / 4900) loss: 1.173295\n",
      "(Iteration 2901 / 4900) loss: 1.115333\n",
      "(Epoch 6 / 10) train acc: 0.585000; val_acc: 0.507000\n",
      "(Iteration 3001 / 4900) loss: 1.270126\n",
      "(Iteration 3101 / 4900) loss: 1.139738\n",
      "(Iteration 3201 / 4900) loss: 0.960612\n",
      "(Iteration 3301 / 4900) loss: 1.165812\n",
      "(Iteration 3401 / 4900) loss: 0.963466\n",
      "(Epoch 7 / 10) train acc: 0.575000; val_acc: 0.496000\n",
      "(Iteration 3501 / 4900) loss: 1.352476\n",
      "(Iteration 3601 / 4900) loss: 1.188220\n",
      "(Iteration 3701 / 4900) loss: 1.289864\n",
      "(Iteration 3801 / 4900) loss: 1.069123\n",
      "(Iteration 3901 / 4900) loss: 1.171000\n",
      "(Epoch 8 / 10) train acc: 0.566000; val_acc: 0.493000\n",
      "(Iteration 4001 / 4900) loss: 1.236990\n",
      "(Iteration 4101 / 4900) loss: 1.001824\n",
      "(Iteration 4201 / 4900) loss: 1.010839\n",
      "(Iteration 4301 / 4900) loss: 1.016047\n",
      "(Iteration 4401 / 4900) loss: 1.215157\n",
      "(Epoch 9 / 10) train acc: 0.627000; val_acc: 0.518000\n",
      "(Iteration 4501 / 4900) loss: 1.257328\n",
      "(Iteration 4601 / 4900) loss: 1.030267\n",
      "(Iteration 4701 / 4900) loss: 1.096493\n",
      "(Iteration 4801 / 4900) loss: 1.059439\n",
      "(Epoch 10 / 10) train acc: 0.608000; val_acc: 0.520000\n"
     ]
    }
   ],
   "source": [
    "model = TwoLayerNet()\n",
    "solver = None\n",
    "\n",
    "##############################################################################\n",
    "# TODO: Use a Solver instance to train a TwoLayerNet that achieves at least  #\n",
    "# 50% accuracy on the validation set.                                        #\n",
    "##############################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "data=get_CIFAR10_data()\n",
    "solver = Solver(model,data,update_rule='sgd',\n",
    "                optim_config={'learning_rate':1e-3},\n",
    "                lr_decay=0.95,\n",
    "                num_epochs=10,\n",
    "                batch_size=100,\n",
    "               print_every=100\n",
    "               )\n",
    "solver.train()\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "##############################################################################\n",
    "#                             END OF YOUR CODE                               #\n",
    "##############################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run this cell to visualize training loss and train / val accuracy\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.title('Accuracy')\n",
    "plt.plot(solver.train_acc_history, '-o', label='train')\n",
    "plt.plot(solver.val_acc_history, '-o', label='val')\n",
    "plt.plot([0.5] * len(solver.val_acc_history), 'k--')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(loc='lower right')\n",
    "plt.gcf().set_size_inches(15, 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multilayer network\n",
    "Next you will implement a fully-connected network with an arbitrary number of hidden layers.\n",
    "\n",
    "Read through the `FullyConnectedNet` class in the file `cs231n/classifiers/fc_net.py`.\n",
    "\n",
    "Implement the initialization, the forward pass, and the backward pass. For the moment don't worry about implementing dropout or batch/layer normalization; we will add those features soon."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial loss and gradient check\n",
    "\n",
    "As a sanity check, run the following to check the initial loss and to gradient check the network both with and without regularization. Do the initial losses seem reasonable?\n",
    "\n",
    "For gradient checking, you should expect to see errors around 1e-7 or less."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running check with reg =  0\n",
      "Initial loss:  2.3004790897684924\n",
      "W1 relative error: 1.48e-07\n",
      "W2 relative error: 2.21e-05\n",
      "W3 relative error: 3.53e-07\n",
      "b1 relative error: 5.38e-09\n",
      "b2 relative error: 2.09e-09\n",
      "b3 relative error: 5.80e-11\n",
      "Running check with reg =  3.14\n",
      "Initial loss:  7.052114776533016\n",
      "W1 relative error: 7.36e-09\n",
      "W2 relative error: 6.87e-08\n",
      "W3 relative error: 3.48e-08\n",
      "b1 relative error: 1.48e-08\n",
      "b2 relative error: 1.72e-09\n",
      "b3 relative error: 1.80e-10\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "for reg in [0, 3.14]:\n",
    "  print('Running check with reg = ', reg)\n",
    "  model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
    "                            reg=reg, weight_scale=5e-2, dtype=np.float64)\n",
    "\n",
    "  loss, grads = model.loss(X, y)\n",
    "  print('Initial loss: ', loss)\n",
    "  \n",
    "  # Most of the errors should be on the order of e-7 or smaller.   \n",
    "  # NOTE: It is fine however to see an error for W2 on the order of e-5\n",
    "  # for the check when reg = 0.0\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As another sanity check, make sure you can overfit a small dataset of 50 images. First we will try a three-layer network with 100 units in each hidden layer. In the following cell, tweak the **learning rate** and **weight initialization scale** to overfit and achieve 100% training accuracy within 20 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 2.363364\n",
      "(Epoch 0 / 20) train acc: 0.020000; val_acc: 0.105000\n",
      "(Epoch 1 / 20) train acc: 0.020000; val_acc: 0.106000\n",
      "(Epoch 2 / 20) train acc: 0.020000; val_acc: 0.110000\n",
      "(Epoch 3 / 20) train acc: 0.020000; val_acc: 0.110000\n",
      "(Epoch 4 / 20) train acc: 0.040000; val_acc: 0.109000\n",
      "(Epoch 5 / 20) train acc: 0.040000; val_acc: 0.111000\n",
      "(Iteration 11 / 40) loss: 2.270022\n",
      "(Epoch 6 / 20) train acc: 0.040000; val_acc: 0.111000\n",
      "(Epoch 7 / 20) train acc: 0.060000; val_acc: 0.112000\n",
      "(Epoch 8 / 20) train acc: 0.060000; val_acc: 0.111000\n",
      "(Epoch 9 / 20) train acc: 0.040000; val_acc: 0.110000\n",
      "(Epoch 10 / 20) train acc: 0.040000; val_acc: 0.109000\n",
      "(Iteration 21 / 40) loss: 2.309562\n",
      "(Epoch 11 / 20) train acc: 0.060000; val_acc: 0.110000\n",
      "(Epoch 12 / 20) train acc: 0.060000; val_acc: 0.110000\n",
      "(Epoch 13 / 20) train acc: 0.060000; val_acc: 0.110000\n",
      "(Epoch 14 / 20) train acc: 0.060000; val_acc: 0.110000\n",
      "(Epoch 15 / 20) train acc: 0.060000; val_acc: 0.113000\n",
      "(Iteration 31 / 40) loss: 2.285026\n",
      "(Epoch 16 / 20) train acc: 0.060000; val_acc: 0.117000\n",
      "(Epoch 17 / 20) train acc: 0.080000; val_acc: 0.113000\n",
      "(Epoch 18 / 20) train acc: 0.080000; val_acc: 0.118000\n",
      "(Epoch 19 / 20) train acc: 0.100000; val_acc: 0.118000\n",
      "(Epoch 20 / 20) train acc: 0.100000; val_acc: 0.120000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Use a three-layer Net to overfit 50 training examples by \n",
    "# tweaking just the learning rate and initialization scale.\n",
    "\n",
    "num_train = 50\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "weight_scale = 1e-2   # Experiment with this!\n",
    "learning_rate = 1e-4  # Experiment with this!\n",
    "model = FullyConnectedNet([100, 100],\n",
    "              weight_scale=weight_scale, dtype=np.float64)\n",
    "solver = Solver(model, small_data,\n",
    "                print_every=10, num_epochs=20, batch_size=25,\n",
    "                update_rule='sgd',\n",
    "                optim_config={\n",
    "                  'learning_rate': learning_rate,\n",
    "                }\n",
    "         )\n",
    "solver.train()\n",
    "\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.title('Training loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Training loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now try to use a five-layer network with 100 units on each layer to overfit 50 training examples. Again, you will have to adjust the learning rate and weight initialization scale, but you should be able to achieve 100% training accuracy within 20 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 2.302585\n",
      "(Epoch 0 / 20) train acc: 0.160000; val_acc: 0.079000\n",
      "(Epoch 1 / 20) train acc: 0.160000; val_acc: 0.079000\n",
      "(Epoch 2 / 20) train acc: 0.160000; val_acc: 0.079000\n",
      "(Epoch 3 / 20) train acc: 0.160000; val_acc: 0.079000\n",
      "(Epoch 4 / 20) train acc: 0.160000; val_acc: 0.079000\n",
      "(Epoch 5 / 20) train acc: 0.160000; val_acc: 0.079000\n",
      "(Iteration 11 / 40) loss: 2.301962\n",
      "(Epoch 6 / 20) train acc: 0.160000; val_acc: 0.079000\n",
      "(Epoch 7 / 20) train acc: 0.160000; val_acc: 0.079000\n",
      "(Epoch 8 / 20) train acc: 0.160000; val_acc: 0.079000\n",
      "(Epoch 9 / 20) train acc: 0.160000; val_acc: 0.079000\n",
      "(Epoch 10 / 20) train acc: 0.160000; val_acc: 0.079000\n",
      "(Iteration 21 / 40) loss: 2.301859\n",
      "(Epoch 11 / 20) train acc: 0.160000; val_acc: 0.079000\n",
      "(Epoch 12 / 20) train acc: 0.160000; val_acc: 0.079000\n",
      "(Epoch 13 / 20) train acc: 0.160000; val_acc: 0.079000\n",
      "(Epoch 14 / 20) train acc: 0.160000; val_acc: 0.079000\n",
      "(Epoch 15 / 20) train acc: 0.160000; val_acc: 0.079000\n",
      "(Iteration 31 / 40) loss: 2.301798\n",
      "(Epoch 16 / 20) train acc: 0.160000; val_acc: 0.079000\n",
      "(Epoch 17 / 20) train acc: 0.160000; val_acc: 0.079000\n",
      "(Epoch 18 / 20) train acc: 0.160000; val_acc: 0.079000\n",
      "(Epoch 19 / 20) train acc: 0.160000; val_acc: 0.079000\n",
      "(Epoch 20 / 20) train acc: 0.160000; val_acc: 0.079000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Use a five-layer Net to overfit 50 training examples by \n",
    "# tweaking just the learning rate and initialization scale.\n",
    "\n",
    "num_train = 50\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "learning_rate = 2e-3  # Experiment with this!\n",
    "weight_scale = 1e-5   # Experiment with this!\n",
    "model = FullyConnectedNet([100, 100, 100, 100],\n",
    "                weight_scale=weight_scale, dtype=np.float64)\n",
    "solver = Solver(model, small_data,\n",
    "                print_every=10, num_epochs=20, batch_size=25,\n",
    "                update_rule='sgd',\n",
    "                optim_config={\n",
    "                  'learning_rate': learning_rate,\n",
    "                }\n",
    "         )\n",
    "solver.train()\n",
    "\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.title('Training loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Training loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## Inline Question 2: \n",
    "Did you notice anything about the comparative difficulty of training the three-layer net vs training the five layer net? In particular, based on your experience, which network seemed more sensitive to the initialization scale? Why do you think that is the case?\n",
    "\n",
    "## Answer:\n",
    "[FILL THIS IN]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Update rules\n",
    "So far we have used vanilla stochastic gradient descent (SGD) as our update rule. More sophisticated update rules can make it easier to train deep networks. We will implement a few of the most commonly used update rules and compare them to vanilla SGD."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SGD+Momentum\n",
    "Stochastic gradient descent with momentum is a widely used update rule that tends to make deep networks converge faster than vanilla stochastic gradient descent. See the Momentum Update section at http://cs231n.github.io/neural-networks-3/#sgd for more information.\n",
    "\n",
    "Open the file `cs231n/optim.py` and read the documentation at the top of the file to make sure you understand the API. Implement the SGD+momentum update rule in the function `sgd_momentum` and run the following to check your implementation. You should see errors less than e-8."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  8.882347033505819e-09\n",
      "velocity error:  4.269287743278663e-09\n"
     ]
    }
   ],
   "source": [
    "from cs231n.optim import sgd_momentum\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "v = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-3, 'velocity': v}\n",
    "next_w, _ = sgd_momentum(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [ 0.1406,      0.20738947,  0.27417895,  0.34096842,  0.40775789],\n",
    "  [ 0.47454737,  0.54133684,  0.60812632,  0.67491579,  0.74170526],\n",
    "  [ 0.80849474,  0.87528421,  0.94207368,  1.00886316,  1.07565263],\n",
    "  [ 1.14244211,  1.20923158,  1.27602105,  1.34281053,  1.4096    ]])\n",
    "expected_velocity = np.asarray([\n",
    "  [ 0.5406,      0.55475789,  0.56891579, 0.58307368,  0.59723158],\n",
    "  [ 0.61138947,  0.62554737,  0.63970526,  0.65386316,  0.66802105],\n",
    "  [ 0.68217895,  0.69633684,  0.71049474,  0.72465263,  0.73881053],\n",
    "  [ 0.75296842,  0.76712632,  0.78128421,  0.79544211,  0.8096    ]])\n",
    "\n",
    "# Should see relative errors around e-8 or less\n",
    "print('next_w error: ', rel_error(next_w, expected_next_w))\n",
    "print('velocity error: ', rel_error(expected_velocity, config['velocity']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have done so, run the following to train a six-layer network with both SGD and SGD+momentum. You should see the SGD+momentum update rule converge faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running with  sgd\n",
      "(Iteration 1 / 200) loss: 2.760209\n",
      "(Epoch 0 / 5) train acc: 0.093000; val_acc: 0.098000\n",
      "(Iteration 11 / 200) loss: 2.292984\n",
      "(Iteration 21 / 200) loss: 2.114119\n",
      "(Iteration 31 / 200) loss: 2.216689\n",
      "(Epoch 1 / 5) train acc: 0.256000; val_acc: 0.201000\n",
      "(Iteration 41 / 200) loss: 2.126647\n",
      "(Iteration 51 / 200) loss: 1.986880\n",
      "(Iteration 61 / 200) loss: 1.913043\n",
      "(Iteration 71 / 200) loss: 1.968741\n",
      "(Epoch 2 / 5) train acc: 0.290000; val_acc: 0.238000\n",
      "(Iteration 81 / 200) loss: 1.893131\n",
      "(Iteration 91 / 200) loss: 2.040442\n",
      "(Iteration 101 / 200) loss: 2.057427\n",
      "(Iteration 111 / 200) loss: 1.888310\n",
      "(Epoch 3 / 5) train acc: 0.330000; val_acc: 0.246000\n",
      "(Iteration 121 / 200) loss: 1.945007\n",
      "(Iteration 131 / 200) loss: 1.855716\n",
      "(Iteration 141 / 200) loss: 1.804009\n",
      "(Iteration 151 / 200) loss: 1.777006\n",
      "(Epoch 4 / 5) train acc: 0.345000; val_acc: 0.274000\n",
      "(Iteration 161 / 200) loss: 1.794924\n",
      "(Iteration 171 / 200) loss: 1.762803\n",
      "(Iteration 181 / 200) loss: 1.927736\n",
      "(Iteration 191 / 200) loss: 1.775683\n",
      "(Epoch 5 / 5) train acc: 0.380000; val_acc: 0.280000\n",
      "\n",
      "running with  sgd_momentum\n",
      "(Iteration 1 / 200) loss: 2.571494\n",
      "(Epoch 0 / 5) train acc: 0.102000; val_acc: 0.107000\n",
      "(Iteration 11 / 200) loss: 2.304714\n",
      "(Iteration 21 / 200) loss: 2.096630\n",
      "(Iteration 31 / 200) loss: 1.960323\n",
      "(Epoch 1 / 5) train acc: 0.311000; val_acc: 0.287000\n",
      "(Iteration 41 / 200) loss: 1.821489\n",
      "(Iteration 51 / 200) loss: 1.691696\n",
      "(Iteration 61 / 200) loss: 1.791583\n",
      "(Iteration 71 / 200) loss: 1.708405\n",
      "(Epoch 2 / 5) train acc: 0.373000; val_acc: 0.332000\n",
      "(Iteration 81 / 200) loss: 1.809073\n",
      "(Iteration 91 / 200) loss: 1.717186\n",
      "(Iteration 101 / 200) loss: 1.733293\n",
      "(Iteration 111 / 200) loss: 1.517235\n",
      "(Epoch 3 / 5) train acc: 0.464000; val_acc: 0.353000\n",
      "(Iteration 121 / 200) loss: 1.410725\n",
      "(Iteration 131 / 200) loss: 1.569099\n",
      "(Iteration 141 / 200) loss: 1.410328\n",
      "(Iteration 151 / 200) loss: 1.383605\n",
      "(Epoch 4 / 5) train acc: 0.470000; val_acc: 0.335000\n",
      "(Iteration 161 / 200) loss: 1.437428\n",
      "(Iteration 171 / 200) loss: 1.206950\n",
      "(Iteration 181 / 200) loss: 1.371172\n",
      "(Iteration 191 / 200) loss: 1.307008\n",
      "(Epoch 5 / 5) train acc: 0.548000; val_acc: 0.349000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-16-a73acf083950>:39: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  plt.subplot(3, 1, 1)\n",
      "<ipython-input-16-a73acf083950>:42: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  plt.subplot(3, 1, 2)\n",
      "<ipython-input-16-a73acf083950>:45: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  plt.subplot(3, 1, 3)\n",
      "<ipython-input-16-a73acf083950>:49: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  plt.subplot(3, 1, i)\n"
     ]
    },
    {
     "data": {
      "image/png": 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7O3bt2pX2oRBRlYMPPhhjx9qZuJ2IKAsYwBERWdDY2IiJEyemfRhERERUcEyhJCIiIiIiygkGcERERERERDnBAI6IiIiIiCgnGMBFtXUNcOMUYHlz6f+ta9I+IiIiIiIiqhM1B3AiMk5Efi0i20TkSRH5vMd6p4jIlvI6/1X7oWbA1jXAnZcAe3cA0NL/d17CII6IiIiIiBJh0gO3H8DlqvoeAH8F4CIROaZ6BRFpBvAfAOar6nsBfNRgf+m791qgp2vgsp6u0nIiIiIiIqKY1RzAqeqLqvpo+efXAWwD0OJY7RMA1qrqC+X1Xq51f5mwtz3aciIiIiIiIousjIETkQkAZgD4neOhvwRwqIj8RkQ2icjf+mxjsYhsFJGNmZ0Id6THRKBey4mIiIiIiCwyDuBEZASA2wBcqqqvOR4eCuB4AGcAmAfgn0XkL922o6orVbVVVVtHjx5telh2VQqX7N0BQAY+1tgEzF2WymEREREREVF9GWryZBFpRCl4u0VV17qs0g5gt6q+CeBNEbkfwLEA/ttkv4mqFC7pH/umKAVxCowcVwrepi1K8QCJiIiIiKhe1BzAiYgA+C6Abar6dY/V7gDwLREZCmAYgBMB3FjrPlPhVrikErxd9kQqh0RERERERPXJpAduFoDzADwuIlvKy64GMB4AVPXbqrpNRH4BYCuAPgCrVDVfUQ8LlxARERERUUbUHMCp6gYMGhDmut4KACtq3U/qRo4tj31zWU5ERERERJQgK1UoC23uslKhkmosXEJERERERClgABdk2iLgzJtKY94gpf/PvImFS4iIiIiIKHFGVSjrxrRFDNiIiIiIiCh17IEjIiIiIiLKCQZwREREREREOcEAjoiIiIiIKCcYwBEREREREeUEAzgiIiIiIqKcYABHRERERESUE5xGIKJ1mzuw4u6nsbOzC2Oam7Bk3mQsmNGS9mEREREREVEdYAAXwbrNHbhq7ePo6ukFAHR0duGqtY8DAIM4IiIiIiKKHVMoI1hx99P9wVtFV08vVtz9dEpHRERERERE9YQBXAQ7O7siLSciIiIiIrKJAVwEY5qbIi0nIiIiIiKyiQFcBEvmTUZT45ABy84e+iB++tbfoe+akXhp+V/gkfX/N6WjIyIiIiKiomMRkwgqhUoqVSg/fvDD+Gf9DoajGxDgSOzCyE1fxCMAZs7/bLoHS0REREREhSOqmvYxDNLa2qobN25M+zACvbT8L3Akdg1ejtE4cvkzKRwREREREREVgYhsUtVW53KmUBp4hw4O3krLdyd8JEREREREVA9qDuBEZJyI/FpEtonIkyLyeZ91Z4pIr4j8Ta37y6KXZbTH8lEJHwkREREREdUDkx64/QAuV9X3APgrABeJyDHOlURkCICvArjbYF+ZtOO4JejSYQOWdekw7DhuSUpHRERERERERVZzAKeqL6rqo+WfXwewDUCLy6oXA7gNwMu17iurZs7/LJ44/l/wEkajTwUvYTSeOP5fWMCEiIiIiIhiYaUKpYhMADADwO8cy1sAnA1gDoCZAdtYDGAxAIwfP97GYSVi5vzPAuWA7cjyPyIiIiIiojgYFzERkREo9bBdqqqvOR7+BoArVbU3aDuqulJVW1W1dfRo97FlRERERERE9cyoB05EGlEK3m5R1bUuq7QC+ImIAMAoAB8Skf2qus5kv1m2bnNH/zxxY5qbsGTe5P754wAAW9cA914L7G0HRo4FJp0G/PGeA7/PXQZMW5TeCyAiIiIiosyqeR44KUVlPwTwiqpeGmL9HwD4mar+Z9C6eZkHzmnd5g5ctfZxdPUc6HBsahyC6xdOLQVxW9cAd14C9HR5bqMLB2Fp92ew8e2nDg7+iIiIiIioLsQxD9wsAOcBmCMiW8r/PiQinxORzxlsN7dW3P30gOANALp6erHi7qdLv9x7rW/wBgBN+DOWDF2Djs4uXLX2cazb3BHX4RIRERERUc7UnEKpqhsASIT1z691X3mxs9M9OOtfvrc91HbGyB4AB4I/9sIRERERERFgoYgJHTCmuQnzGzZgw7BL8OxBn8CGYZdgfsMGjGluKq0wcmyo7ezUww/8XB0Ubl0D3DgFWN5c+n/rGpuHT0REREREGccAzqJvHPNHfLVxFcY27EaDAGMbduOrjavwjWP+WFph7jKgsWnAc5wjEPfpMNyw/0ARk/7grzJ+bu+O0rP27ij9ziCOiIiIiKhuMICzaOb//BuapHvAsibpxsz/+bfSL9MWAWfeBIwcB0Cwr+ko3Np3Ktr7RqFPBe19o7C050Ks7zup9NzGIVgyb3LpuW7j53q6SsuJiIiIiKguWJnIm8q8xrhVL5+2qH+agFPb7kNHdxeACwY9pcU5BUGYbRMRERERUaGxB84mrzFuHsu9ip4IgAeXzgEAzGq7DxOX3oWXMMp929LAMXFERERERHWCAZxNLmPc0NhUWu6if3yby/LKnHIdnV1QANd1fxRdOmzAegoA2ovKmLj9d1zMII6IiIiIqMAYwNnkGOOGkeNKv09b5Lr6knmT0dQ4ZMCyyrg355xy6/tOwpU9F+IljAYg6JOGQXM4DO19C/t+7h4srtvc0d+bN6vtPs4vR0RERESUQ6LqrIOYvtbWVt24cWPah5GIdZs7sOLup7Gzswtjqsa9TVx616AKlUApvXJ72xnoW96MBpc1+iBoWN45aB9XrX18QEDY1DgE1y+cyjnmiIiIiIgySEQ2qWqrczmLmKRswYwW1yBqTHMTOlzGyFXSLnf2HY6xDbsHPb6z73A4R9w5e/MAThJORERERJRHTKHMKL/0SgBYNeyT2OcYE7dPh2HVsE/2/15Jm3QLBAHvIipERERERJRNDOAyasGMFly/cCpampsgKE0rUJ3yOP2MxVimiwfMIbdMF2P6GYsBYEARFC9eRVSIiIiIiCibmEIZM+cYt/cfPRq/fmrXoDFvbrzSKyuPAf+Aj90913VbbmmT1QZMEk5ERERERLnAAC5GzuIhHZ1d+NHDL/Q/3tHZhavWPg4ANY1F8wvw/NIjB00SnmFeRV6IiIiIiOoRUyhjFNQLBhwoJmKbV3pkS3MTHlw6JxdBkHMuvErAyykQiIiIiKheMYCL0c7OLsxv2IANwy7Bswd9AhuGXYL5DRtc1wsjylxuQUVQ8sCvemZYnP+OiIiIiIqEKZQx+tSI3+OKnlUYLt0AgLGyG22Nq4Ce0sTcFWGKibilY/qlX1aPhctr+qFXYOsX8FanXI5sasSb3fvR01uaL880ZZWIiIiIKG0M4GJ0ReNqDN/fPWDZcOnGFUPXYH13KYAL2ysWai63rWuAe68F9rYDI8diwdxlWLB0kev28jC2LGguPCdnkNvZ1TNona6eXly6egtW3P10Jl8zEREREZEfplDGaHjXS67LxzTscZ0aAFvXADdOAZY3l/7fuqb/OYG9UVvXAHdeAuzdAUBL/995yYBtVORlbFnUNNAwYw4ranrNPu8PEREREVESag7gRGSciPxaRLaJyJMi8nmXdc4Vka3lf78VkWPNDjdnRo51Xdwwciy2t50xsJhIQADm1evUv/zea4EeR5DX01Va7uDVm3f5msfSGSvmERgFzYXnFHVi8kjj6SIEyEREREREcTFJodwP4HJVfVREDgGwSUR+qap/qFpnO4CTVfVVEfkggJUATjTYZ77MXVZq5FcHVo1NpeVOfgHYtEVYMm/ygPRAYGBvlO5th7gcgu7dAVneXAom5y4Dpi3yDHR6tbaxYkbpmJXAqPLaK4ERAExb5DtVgpNXyqWf0EFfwPtDRERERJSEmnvgVPVFVX20/PPrALYBaHGs81tVfbX868MA3LukimraIuDMm4CR4wBI6f8zb3Jv8O9td99GeXlQb9SfMMr16aWgbmCPUZiiKc7eKa9qjsbpmBF6DoO4pVw2NggOHd7o+Zww5wJA4PtDRERERJQEK0VMRGQCgBkAfuez2mcA/NxnG4sBLAaA8ePH2zisbJi2KFwPzcix5fQ8l+Vlfr1R13d/FNc3Hqh46aocGC2Zd/eg3jw3ld4pvwqYoYqr+LEYGPlV3nS+BiDitAoh3h8iIiIiorgZB3AiMgLAbQAuVdXXPNZ5P0oB3ElujwOAqq5EKcUSra2tanpcuRMl3dLFxrefiqWvAVcMXYMxsgcChbjlVO5tHxToNIj0p09WaxDBxKV3uT5eCdJMS/0/dPAoHIldg1eqMTDyCnKNp1UwfH/qUR4qnRIRERHljVEAJyKNKAVvt6jqWo91pgFYBeCDqrrHZH+FVumlq5oGoDJmLYzSGLnu/ukJNgy7BGNl9+AVy4FRdaDj1jsFHBgT5xbcAehvmJuU+r+u+6P4auMqNFX3HMYUGEUZTzeI4fsTRRECn6jzFhIRERFRODUHcCIiAL4LYJuqft1jnfEA1gI4T1X/u9Z91Y2w6ZYunD1Mq4Z9El/Ub2No71sHVvIIjML2yDlVgosoqYnOlMv1fScBPcDVw36KI7E71sDImMH7E1ZRAh/j1FqiHCnCTRciIsoP0RANddcnipwE4AEAjwPoKy++GsB4AFDVb4vIKgAfAfB8+fH9qtoatO3W1lbduHFjTcdFVRwTe4cNjCYuvQtBV0VT45D+IipRGi9e2xYA29vOCDy2opvVdp9rj2ZLcxMeXDonhSOqDd9nqhde42v9pjwhIiIKQ0Q2ucVONffAqeoGwLVyffU6FwK4sNZ91L0aA7B+NfYYeaVFDhFBn+qgIM1Gqf/Q1SALrpYxhVnE95nqBXubiYgoaTVPI0AxS3HiaLdy/E2NQ/Cvi47F9rYzsGTeZKy4++maJv1eMm8y/mbYb7Fh2CV49qBPYMOwS/A3w34bvhpkwQVO2J4TXtdQre+z1zQWRGkryk0XIiLKDyvTCFAMUpw4Oko5/qhjtBYMeRAfblzVPzZvrOxG25BVGDrkWACL6nYsSeV1d3R2QYAB6YcmgU9ajKt+VinKuEAqJvY2ExFR0moeAxcnjoEDsLwZ8BpFtLzT/v5CpmuGGaPlDMLef/Ro/PqpXeVpAz7vMW3AOKw7ZfD8dIPGkpimlWaQ2xiaShDXUkdBrJeijAukYuIYOCIiiov1MXAUsyQnjq6ka1Z6/CrpmsCg4CgoXcitt+RHD7/Qv947dJf7yMm97cFjSVyOc/8dF+Nf1j+JH75xQqZ77Px6Ft1edyV4q6cAxescMUWNssxmbzMREVEYDOCyKsmJoyOkawalC7kFI9V26ijP+el2/imgoe5ynEN738KFfT/CD3BCZlPrglIA0wxQspKy6neOmKJGWWc0xyQREVFELGKSVdMWAWfeBIwcB0BK/595k710wa1rgBunlFI13Xr6gFKaokNQcYqdnV2Y37BhQJGS+Q0b+te9Yf8i7NNhAzdaDkwDC3i4HA8AjJED88NXeuzSVl104/I1j3n2LALpFS6pBE0dnV1QHAia0igQ4tf7arsgChEREVGesQcuy+KaONqZiujFJV0zKF3oUyN+jyt6VmG4dAMoFylpXAX0lCbt9pu4e0mv+1iS/oa6R1rpTj184O8pp9Y5e5O8JkWvHGfUydBtyVL5c79eSKaoERERER3AAK4euaVMOvmka/qlC13RuBrD93cPWDZcunHF0DVY330SAOCXQ07GnLP+cdA2AhvqLmml+3QYbtg/OM2zFrbSCYPSSCsqx5lWgGI7ddPk/AWlSTJFjYiIiKiEAVy9qK7e6FrdskJKPV2TTiutv3bx4GqPPpUgh3e95LrVMQ17IEBgw963oV7Zf3nf+5qOxLI3P4L1fX/dv0qUnqvqgGNkUyPe7N6Pnt7SuTEZTxcmAHIep9/rDgqMag2cbI4tMy31n1YvJBEREVHeMICrB6FTJscBlz3hX5US8K9Y6ZHm2DByLLYvP8P8tVSllQ4HcNLmDjxUQ/DiDDg6u3oGreNMJ3QGfCJA576eQfv1CoyGiKBP1eg4nYGRSeBkM2gyTce03QuZleIsROSOn1EiotpxHrh6cOMU70IlFY1NB4qkeK0/clzpf6/H3II/57YzwmtuMScBsL3tDNe5nqpVz/tkc16ooDnQapmXr7qhZKsRNXHpXV6zFmJ7m4XAPQLOy0U2MMCIDz+jREThcB64euZRvbFEBiN7F2UAACAASURBVKdIeq3vt529O0oVLUeOBY79BPDHezI92XbYcV4NIpi49C40iHgWIwEG9jaF6U0K2zgMGqdWy7x8l63egktXb+mfJNzGXHNZKvWfpeIslF1BNzZMUoLJHz+jRERmGMDVA89Jwcu9ZqHXL1el9OzN09Jjj90aqcctjTvdXgGHUyVo8wveKqqDqaAxbWEbh17HGRRY+s3LV1nbZqM0S2PYkpxXj7005tI4h0GfQQYY8Upz7ksioiLgPHD1YO6yUhpjNb9Jwf3Wd3vMqTIJeAjW5yKrnt/uximl3124zS3W2CA4dHgjBKXxalGF7W3yaxyGOU6gFFAq3ANL57x8fmzNm7dgRguuXzgVLc1NEJRSONNKh0pqXr0szaOXpuo5D2e13Rf4+qvXn/6le7DkPx9L/BwGfQYZYMQrrbkviYiKgj1w9cBRvTEwrTHM+kEVLX3TNg+Ieqfb9269X/EVx2sNSnOcuPSuUMdfEaW3KUrj0HmcXj1uXgVSwvQ02mqU2iz1b9Irk1RvYD330lTen47OLgjC9+pGLR4UV+9c0GcwSynBRZSlHnsKxkwDouxhAFcvok4K7rd+9WOeBU8GTwLuJkowE5h66Da/XaU30OW1+AUcQZUknVUov3HMHzHzN18A7ggOkKM2DquP0yuw7FN1LRbi1lAKu9+0mI4/SqqiZb320jjfH+ftBL8gNuwciTs7u2Idhxb0GWSAEa+05r6k6DgelCibGMCRGZfJtQelZ/rMGxclmAns8ail+IoHrwaca1rg1jXAndeE6vnz23aYxmGY8+UMOD5yfAt+/dSuQb0lUfabJBs9W1F6A2stZlHLe1GERmqYIMw0uB3T3BRrD2fQZ5ABRvxs9thTfOo504AoyxjAkZmgdMuAtMYowUxgozCo+EoEkRpwNfT8hd62Q9D5cgs4btvUMWCKA5PqmGHnwnNjq/KmTSbFLGp5L4pw5zrM++A3xikopbdyDi9bvaXm/QcJ8xlkgEHE8aBEWVVzACci4wDcDOBIAH0AVqrqNx3rCIBvAvgQgH0AzlfVR2s/XMokv3TLgOAmSjAT2OMRpjcwgtANuBp6/mptHAadr6C7pSbVMf3GL0Ud+1RL5U1bqZ7VgaTbmMLq8+XXeDF9L/IqKAjz69V1C3obGwQjDh466EZAZYyd2/5tYIBGFIzjQYmyyaQHbj+Ay1X1URE5BMAmEfmlqv6hap0PAphU/ncigP9T/p/qRYjgxrchVZV++cumI7Fs2Efwn91/3f/wgMZi1GIttoTp+fNJI43K73yZ3C0NCjiCUueijn06tfe/8Fd3/CNwx+4B56SWFNOwvXvOQNJreoiwxSziei+yzO39qaTmtgT0xIadI7EyQX0eUn6JiozjQYmyqeYATlVfBPBi+efXRWQbgBYA1QHcWQBuVlUF8LCINIvIUeXnUj0wSWt0pF8O73oRbY2rMGLYUPzwjRPcG+pRi7XYENTzF6E6pimTu6VegUVHZxcmLr3Lq95oqG04l89v2IC2xlUYju7SgqpzsmBG6ZyETTGN0rsXtoiGjWIWad+5jjL+Lsq6puPDovQCK8IHh0R5l8UxsxwPSpRNVsbAicgEADMA/M7xUAuA6tZ7e3nZoABORBYDWAwA48ePt3FYlAUmaY0u6ZdDe9/C8pG3YfkXv2T5QA0E9fxFHCNnIo6AA/CcLMJ1G2G2fcXQNRgu3QNXcqTWhm0gRElVDNP7ZauYRZp3rqMEtbWM1Ysr/dBr4vmW5iY8uHSO9f0RZUWWx8wy3Zgoe4wDOBEZAeA2AJeq6mvOh12e4toWVNWVAFYCQGtra9j2ImWdSVqjxaqSsfPr+UvwddgOOKKIMvZpjOx2XU/3trt+afiJkqoYND2EaTELrwqgld/ff/RorLj7aVy2ekusd7KDgtoo4wCTVNS00yBZ7Hkhc1He16KOmSWieBgFcCLSiFLwdouqrnVZpR3AuKrfxwLYabJPSonJGK5a0xotVpUEYHUcmpPvH2rbryOArQIpfndRBIhUhdK57RcxCi0YHMT9CaNwZMTjjpKqGGl6iIjCVAA1ucMepTHoFwhFHQeYpDTTTtMKorLc80K1i/q+1uvNiyTxRgkVSUOtTyxXmPwugG2q+nWP1dYD+Fsp+SsAezn+LYcqY7j27gCgB8YrbV0T737nLiulW1artapkLa9h65rSROXLm0v/e6xb+UPdUQ56Kn+o123usP86YrZgRgseXDoH29vOQItHo7mluQnb287AlmtOw+Zlp2F72xl4cOmcwD+E1du+oWcR9umwAY/v02G4vvujkY95ybzJaGocMmCZV2/gghktuH7hVLQ0N0HKr8UZvFWKaExcehdmtd134H0M4HcHPczjfgKvMQe/VNao4wCTFOW9tCnq+bXJ5LrIslo/R0UR9X31+8ySuTQ/40RxMOmBmwXgPACPi0hlwp6rAYwHAFX9NoD/h9IUAs+gNI3ABQb7o7QkOIZrAJtVJaO+hgiFRwJTX6K+jhh7CqOIcwzXxrefiqWvlcbCjZE92KmH44b9i7Dp7adG3lbUtFGTqRQq67jtK+gOepwVQp383juv+dWqpVVlLq2CCWmmrwVdF3H2GtjctnOOyDe796Ont9S7W4+9ilE/76z2GK8spaiyJ5BsMKlCuQHuY9yq11EAF9W6D8qINMei2aoqGfU1RAj43P4gz2/YgCv2rQGW7zkQhF32RPBxJlixMkicjelSY6Ub67tP6l/W1DgE19fYWAlKGw37BzPM2DGvAC8o/S+OCqHVy4PG3wXNr+Y3DjBJaRRMqCW4ttUI87su4kyvdNv2kp8+hi/d+WSotOjKNirXU/WUD9VzRFbU23iuqJ/3uG9e1HvQkJUUVaZMky1WqlBSwSU8hsuqSm+W14gur9cQIeBz/qHuL5Evg0vkBwZhafV2eoirMZ1kT0uUP5hBf+T9ArygO+hh7rB7NbKCGoNB4++qxTkOMGvCNlqjNrZtNsL8ros4ew3ctt3Tp3h1Xyn4CnpNblM+BKmn8Vy19KjF9X3LoCHZ8bV+3ztZ6gmsV0W5mVHzGDiqIzkawzXAgHFvLvxeg1dg57LcOW7Ht0R+kDxV3gw5RtBL9Zi4MGPoahVlLErQOBS/AC9ofF3Q435jNILGhkV5jWHGAeaF3zirKGNeoo69szluze/9iLPXIMw2/F5T2LGU1eppPJftz5nJmMKijrOMIqnxtUHfO0n3BNb7WFSnIo2FZA8cBbM5Fi2IzfFfbr1ZFSPH+W87zOTc5eNcMHIsWmZejEv/MKl0R6dhj/s2wwRhtfR2pjFmLkOpnkGi/MEMumsedBc36A663+N+jazKHGhedw2jNgqi3unP4h3LoF6FKHe6o/YI226Eeb0fcfYa+M37WM3Wa63H8Vy2etRMe9Cykj6YpqSyPoK+d5LuCaz3nlenIvWAMoCjcGyNRfNjOyjwDJgkeDyaX9DqcpwzH78GD555U+nxGw1STqNOfJ5WIJVyqmeUgCLKH8ygP/JxFhrwakx1dHZhVtt9WDJvsudk1nE2CrLUCAgzf93lax7DZau3eKb02Qhqk2qExXm9hZ330a9X2i8AbGwQjDh4aOjxdFFk8YZCnEwbnWlOz5ElSYyvDQqWkyxWU6RgxZYi3cxgAEfZYTsoMB275xW0Bh1n1CDMuc/KPsL0qKUVSKWY6hk1oIj6B9Pvj3ycd3H9GsS2X2MUWWkEhJ2/zmt5hY1Ga9D5thVgxHm9ObftrBzpfE1ObuegUsikJSNjWovCtNHJCpfJCZOlASQz/rtIwYotRbqZwQCOssN2UGASSPkJOk7TlNOg3s7qlEmvfoZaz1nYdMwUC9tEDShs/8GM6y5uUI+I6WusNajISiOgljFXTrYarX7n23aAEbXC6vuPHu1afTTMtt22teLup3HZ6i2DtpVW1cSs3FBIkmmjM63pOeKWxZ7YMMFyUpV2ixSs2FKkmxkM4Cg7bAcFcY3dC3OccaWcOlMmvdRyzqKkY8YVHIdQS0CRRmn6qKobWV49cbW+RpOgIiuNAJOAUQDrDTyv8x13gBE039qPHn6hf92owWP1awpzzaRRNTErNxSSZKPRGed3YBqBVFZ7YrMULEe9brIYENuWpffHlGhAukkaWltbdePGjWkfBiXNLThpbAIqY8uyIs3jvHGKd1VN02Px2vbIce5jBlOacHxW232uAUVLc5PnGLG8sf0aTbbnbCgB6Uw74PUaKvPXuY2JA5K/LiYuvcu1X1wAbG87I9Q2vBpSbu9FGLXM8Zfm58xv3wASPa6sNGqzOqF73N8PXsdWD38HbAj73mble54GE5FNqtrqXM4eOMqOJKtdmkjzOH1TI8XsWKKmsCZR2MaF213Fvxn2W1wrtwHLX7L/fsQYqHr9cTWZM86NSa9F1DuWcTU0g+av82qAJJ0aY9pj6dezUGsaaSWwjTJZd5o9XX77vvFj0xN7n7PUy5NGb2eY/cXZ45xmT2xWAndTYa+bekxNzjsGcJQtKQUFkaV1nJ7pmx69ZFa2ndCE7SEDJWdA8akRv8cXdRWGdr1VWsFmJc4Yq3yGaTh5NSCiNrpqmajaue8wd7XjbPAGnZOspMaYprv5NaRsNE7DTtZtO3U2qEEcVGG0su8k32fbjdosBgWmrzHOQMrv2Oql8m5S6jE1Oe+YQkn5llIaX2pM0zf9zleaqaEm+46a+um2b69zYrptn329hFG4rvujWN930oBVwqQARU0fipIeY5JKw7SmEpOGul8KZti526Jye39splQFbStMaqhJOlet74eNdNjqY8hiiprpa7T9ma9+r7xapwJ49sTaOJ/1+D0WlKIe9w2HLN7cyAqmUFLx5GgyaSPOAOPYTwB/vCd60Bp0vtJMDTWZDiFM6qdXkBZ0TmxWRnXs60jsQlvjKqAHA4K4MHc8a5m4GwjXa2FyR95vLruJS++qmz/MJulufj0Lbr17zvnWqqtQevVkObm9bzZ7uoKuKa/U0Fobj0GFXtIo4JN0ilrYBrHpa7Q5pUbYMZ5x98TWY2+UVxXk6vTruHoh67HH0wYGcJRfKU8mnQi3AOOxW2vrFQtzvtJKDTUJlIJSP/2CtKBzYjOt1GVfw6UbVwxdg/XdBwK4MA2nWhpdYYMKk8aLXw+Rgn+Yw/BrENcyHtFksm5b466Crimvx/tUjXu6Ort6Bq1TPeG73zn0Cpj3de+PfEMiyaAgSoPYNOXX5pQaYcZ4Vh9bXOMCs1J5N0nO99Ht5k9cNxySrNxbpJuIDOAov1KcTDoxNoNUz/O1A1jenG4KqkmgFDSlgd85DLqGbE6X4LGvMbKn/+ewDac457IxabwEzWUHcGB8kDBj/cKeO+e2ok7WbUvQNWWj8EvQ+DmnMD0LXucvzBhCt9eSVFAQpUFsoyfLZEqNMCmTQDxTgXhJe64wv4AjzmCk+n2cuPQu13XiuOEQ582NIvfuMYCj/DLtHUlz/FzYfdsMUr3OFwBAw6WgxnXOggIlv/0GpX76ncOga8hmWqnHvl6WUZEbJ3GmD5k0XpzH5dUgK3IqkpuojS6bPQvObaVxNzromjK55pwNtDDBm5PfTYXq8zer7b5BPXomvXlxBQW1pFjHcQ0EHUfYHuKwY89sXdtpFkTyCzgAJBaMJHnDwWtfDSLGqfdFrq7JAI7yy6R3JM3xc277XvcPwM+vBLpeHRgk2EzhcztfTn69e3GeM79AKcx+/VI//c5hmGvIVlqpx76OPPM6bJ8WLU0MiK/RZdp4cTZ46y0VySlrd4Djum6C9gnEU0G01qkVnEzGntbSmxfmNdYakGQlBTDoOKKmTPqx/TlL43MC+AcclZ/dHrN9rEnecIhz/F2RxzOyCiXlW609QrarC0YRZTJuwG5lyOrz5Vfja3ln+OOO+5zZqDLpdw6T7Imts6qpWa28l6QiVbTL4lgSryqK1aoLvZhM+O71XrppMTw/Jp+dtCbXjnocfu9d1KyEonzO/KqCAu5/taNURY1aVCapz3uYNOha3ssiXBesQknFVGvvSJrjwcKkP1Z6wioBiq1Gf/X58gyMPHr3gtI5g4KTWoMX0zTSoDTIOAu3uL3muG8QRDmWmIPHNFORsqIod4DT7En0a0h69fJ4VbA0mfA9zBjPijDnx+91BaV++T03zs9dlOsg6Di83rtaGtdJf87iCoSCei1Nx4pG+Qwn2QsZ1/i7tMczxskogBOR7wH4MICXVXWKy+MjAfwIwPjyvr6mqt832SeRFTbGg8Wy7yqVACWuACNqCqpfKmJQmqNJ+qWNNNI0qmvW8prjCrJSTBlOKxUpK7KSzmYqrbEkQY1OrwaaV2+TSXATplJfNb/zE/S6/AKSMA3xuD53Ua8Dv+Ow2biO+3NW69QUUYOmoHNicr7yUu3R5ntZ5JuIDYbP/wGA030evwjAH1T1WACnAPhXERlmuE8ic3OXlYIVP5VesDT2DdQ2zg0oNdZvnFLqSbxxSul3N9MWldIIR44DIKX//VIz3Y67EvD5VXoEgh/347bfhkag+83g15imqK+5EmTt3YEBNxFsvDaT809GlsybjKbGIQOW5fEOcFo9iUFjghbMaMH1C6eipbkJglLvTVCq4IIZLXhw6RxsbzsDDy6dE7niYuW5/7ro2EHvrZPX+Ql6XV6N1THNTYHPjZPN6yDqe7ducwdmtd2HiUvvwqy2+7Buc0f/Y3F+zipBWEe5MFNnV8+ASq6A9/mP+l75nRO3xz5yfGkeRbdz4pREtcfKOaoEqn7H48X2e2nyec8yox44Vb1fRCb4rQLgEBERACMAvAJgv8k+iaxwptV5ZeLHMSWBc99NhwLdbwC93QfWqbVUfZQCKZVjCdsD45eKuHax+3Mq588kDdLrfHW9cuA1pjWBu1+PWdTXHOe8hvUw5UYIaYzhKsod4LR6EsM0Ok16m0yuier31mtsnNf5CXpdfj0xl63eEmmbNtm+DsK+d0E9WW6fs/cfPRor7n7as0Jo2Pc+bKEct/NfS9Dkd06qH4vauxfnZ9hm717Qd2YWx+KmIe4xcN8CsB7ATgCHAPiYqva5rSgiiwEsBoDx48fHfFhEMBsPZnPfgL3UObcgoK/HXrDjFfAFpTmapkE636vK66lIYwL3oLTEqK85ziDLZjVTp5wUZklzDFcR0kjTGksSZ6PT7ZpY8tPH8KU7n0Tnvp5IUz5EHVsX9Lr8GrFeAWMc6YLOc5DWdRAmQIgS3ET5PggbGLud/ywFTXG+d1ED1aAgzOs7M2tVfdNkmkIZZB6ALQDGAJgO4Fsi8na3FVV1paq2qmrr6NGjYz4sIge/9MAkTFtUKm6xvLP0f60N4CgFUmwKOn82z29WepOC0hKjvmavYMpGkBXX9R1n2qdlaaadFUEtqYo2xJka53ZN9PQpXt3XEzkNLOr5CfO6vFK/kkwXdJ6DoNfpl+YY5nEvUQOEoM97lO+DMMGW1/mP872qZb6/uD7Dfim/TibplvwePyDuHrgLALRpaa6CZ0RkO4CjAfw+5v0SRWNzwuY0RS2QYkuYSo9+j0cRZ29SFEGBZNTXbDKvYZC4ru840z4tK0o1yDSl0ZMYZwpqmPc+ShpYlPNjs5hKmHTBsKL2dFWz2evlFLUnK+jzHuX7wK3nqnpqCr/zH+Z9TnK+v7g+w27nSFB6j2e13Repwqoffo8fEHcA9wKAuQAeEJEjAEwG8GzM+ySqTRqVCm0LM1k3EE+wE3T+Yp4Qe0CgY5LWF/a5YQJJW2MMbbB5ffefI4+bBRkcW5elapAcwxFNXI1Or2vCKa7GocnrMhkL5cekgRzUMDdpuEdN/wv6vEf5PggzJitofJ5fYZZa37sslch3jgcVHKgs4ExN9qrbGuYay9L3eNqMUihF5McAHgIwWUTaReQzIvI5EflceZUvA/hrEXkcwL0ArlTV3WaHTESenJUlmw4DhjgKvyaZGhqHoOqZJml9UZ4bR1qirVTaOA04Rx6S7g0NISvVIG1WayMzbteEm6w3Dm2mlUVJhXOy2evlZDtF1e3x6h4j5+fRr5Khyfk3eW5aac1+x/Pg0jloaW4aFKRVpyZ7CXONZeV7PAtMq1CeE/D4TgCnmeyDiCKKq0BKlvj1Jpmk9YV5bvX5bDoUGNrkXuGzqNzOUbWM3iDISjXItOZTo8Gc14Rzfi8gH43DWgIjr15gk14dm71ebmymqAb1GA3qBfP5O2oSmJqmBGaxQFItPdZhr7GsfI9nQdwplESUtiKkhkYRNDbNpPS/s/Jk1yulgGXhyvo5x37pkSPHZTqIjdTYienGB8dwZIvzmshjemvUwChM2l4t5yAo+Es65S/o8155fFbbfYPO34CbKgEVh00C0yKmBIZNTQZKvZ61TN/ht24eP8O1YABHRNlj0nj2G5tmWvo/R0U7YuN5jsaV0j6LIOg6MVDEBluRZLFHI0jUwCioF7jWcxCl1ytLjevAmyoB3/smgantoDYLwYvba3LT0tyEB5fOsbrveppmgAEcEWWLaePZr8hJUAAWVCAlK1MYpMl2tcwkU3zD7ivGQD1LhQcSVcRU7oyIGhjF2QscttcrSwJvqgR879uuKFpr0JWV4CXN1OR6SlFnAEdE2WLaePar5rh2sftzwpb+D+qhq4dGqs1qmTH2dPVvv3q8YvcbQG938L48Gmx9e9vx7qV3GTWystoLEau43+dajqdgn9MogRF7gQcKvKkSouKwrYqiJrIUvKSVmlxPKepSmqItW1pbW3Xjxo1pHwYRpWF5M+Baq0pK1RlN3DjFLP3P2RAFSr1PZ95U+tnrsZw3DmNj+n74cXuv3Ljty+O42vtG4aTu0nvd1DjkQMW3AgYEVsX5Pkfl9xmuk/fM2VMDOK7nOuQbYOTkmpm49C6vv5zY3naG63OykHIZRtjjdBvPCMSTrpkUEdmkqq3O5UbTCBBRwW1dU2p8LW8u/R+mFL8prxL0NkrTm5b+95vCwK/nkNzFmZIaVC3Tb18u18k+HYYb9h9orPWX+jaZtqJeZCn12PbnNI3vSENZKz+fBX7TBAROXZMRUaeAyMuUJlGOs56mGWAKJRG5M017qrVXwvYYq2o20v+8qnpmqZGaF3GmpIY97243BhzXSXvf4bhh/yKs7ztpwGo7O7tY2CaMMJPe22RSaTbqfrKUGhpBFseiZVoOqjnbLmaTFVGOs55S1BnAEZE7k4apScPG5hgrr+3H8Yc46UZqEfgF66aNY6/3o5rfjYGq6+RjLmk58xs24OphPwX27nJ/fhECd1upoXHelHEyrTQbBYN3ypAsFbOxKepx1svNCaZQEpE7kzvVpmlK0xaVxsYs7yz9n4fGkGl6ZprSSgOLMyXV7f1oaASaDhu8rwDOtJz5DRvw1cZVOBIewRuQ/8DdZmpokiloQdeNzc8pe90pY3xTQR2iplymJS/HmTT2wBGRO5M71fXYsIm75zAuYXq64izSEVdKqsX3w3ln++phP0UTur2fkJfA3Y/t3qWkUtCCrhubn1P2ulOO5WVKk6DjNC3EkpdCLk4M4IjInUnaU702bHIwTgLAwIBMGgB1TLha3VBPepxP5dhc66kh2jVk8f0YkJaz/FzvFUeOy0fgHiSvN2HCfPfYui6STA0lsixqymVagY7fcZrOfZeVufNqwQCOiNyZ3Klmw8ZcXL1ezoDMGbxVVBrqSY7zCSr9n5VryDNISKEsflzyehMmye+eLPe6c2oLCiHseLG0Ax2v4zQtxJKXQi5uGMARkbda71RnuWGTBzZ6vbwacGHL61ca6qY9MVEakn7HlqWerTzfoAj7fuT1NZp+90QNfLLY657j6piUTVkNdEwLseSlkIsbBnBEFI8sNmzywrTXy68BFybwqm6om/TERG1Ieh6b2O/ZMumhyOsNiijvR15fI1D7d09RAh9WxyTLvAKajs4uTFx6V2pjx8Y0N7lO3B22wInp89PEKpRERFlj2uvl14DzCrxkCFwrBJpU7YtaSTLOSdyr2aiwmMdKqVHfD+drBHI3aXUktif5tilKpdi8jl8kV+s2d2BW232YuPQuzGq7L5WJtv0CmjQnATeduDvPE38zgCMiyhrTQMavAecVkJ39bfdgxKQEfNSGZFJTMWS5oR4nk4a9zWkFsiqrgU/Uc5/UjRAb0prCJCcqY886OrsyFyg5VVIqk7RgRguuXzgVLc1NEAAtzU24fuHU0D2Bps9PE1MoiYiyxnT8kV/aYy2pcbWmpEVNvww6NluFGbLaUI+bSTqs7bS8LBbZyGrhlqjnPi/jF4uSsupk8drOytgzZyVIjxrBqYwdM524O68TfzOAIyLKGtPxR0ENuKTGJ9bSkPQ6Npvz1WW1oR43k4a9zaA3qw33rAY+Uc99XsYvFnGsnuVrO0tFNqoDnVlt9+V27FhRGKVQisj3RORlEfEcXS4ip4jIFhF5UkT+y2R/RER1w2SMlUnaI2AvrcntOI79RKmBFnXbQWmPUdLMkkrVzBqT68JmWl5WU1hNPzdBav1c1XLu8zBGs4g94Zavba+AKO1AKc9jx4pCVL06QkM8WWQ2gDcA3KyqU1webwbwWwCnq+oLIvIOVX05aLutra26cePGmo+LiIhq5DYXW2OTnYasybaXN8N9cm8pNVJvnBJtbjZnb92k04A/3pPtHos02bwugt7LLLGVDmdy/uL8TKYp6mc2Dyxf287514BSoJSFcVppTexdb0Rkk6q2OpcbpVCq6v0iMsFnlU8AWKuqL5TXDwzeiIioBrYamnGmNZlsOyjtsZY0s+rUyyym9IWR1Fgym2l5eUlhtXldmFz7eUmJjCqrKasmLF/bzrFnWQqU8jp2rCjiHgP3lwAaReQ3AA4B8E1VvdltRRFZDGAxAIwfPz7mwyIiyoC47u6bNDTjTGsy2XZQYy9LBTqSknTgaWvsZF4a7javC9PPVRHn1SxiYBrDtc1AidzEHcANBXA8gLkAmgA8wNgnFAAAIABJREFUJCIPq+p/O1dU1ZUAVgKlFMqYj4uIKF2mje/q4E8aAB1YqazmhmacvSMm23Zr7E06rfT72sVA06HAkGFAb/eB56RRoMM2vyA/r4Fn0g33Wm+U2Lwu8tLrmLSiBaZFDEptyGLV2ZyLO4BrB7BbVd8E8KaI3A/gWACDAjgiorpi0vh2Bn/O4K2iloZmnL0jptv2S3vsegVoaASaDgO6Xo3WSMhq4zooyM9y4BkkqYa7yY0Sm9dFXnodyVzRglJTeU5Rz7C4J/K+A8D7RGSoiAwHcCKAbTHvk4go+0wa327Bn5taGppxVuKzuW23c9DXAwx7W/TKe1mtShlU0S5PEzanxaQqoM3rIu4Kl1nFibopq1Vnc86oB05EfgzgFACjRKQdwDUAGgFAVb+tqttE5BcAtgLoA7BKVXNaWoiIyCKTu/thgjyTAMTkDnJQqoytu9M2e5/CpD2lkQIU9BrZqxPM5DqxnQ5Xbz0z7HkhIN+ZAhlmWoXynBDrrACwwmQ/RESFY9L49gr+ZAigfemNMUiywWY77dGvcW1zvKLN1M4sj7fJypgX0+skzaArK+ewVnkdo0l2ZTVFPefiTqEkIiI3JilVXqldZ3873Yl7k0yVSTLt0eR1RZlg3CnMa8zihM0mr9m2oHOY1RS/LJ3DWrHnhQD739VZ/cwmjAEcEVFaam18Z3U8TZINtiTPge3ximGDv7hfY1wNIduBvMlx+p3DLAdJRRg3ZGOMZpT3ng37bLL5PZblz2zCRDV7FftbW1t148aNaR8GERFFceMUj1SZcaUANa9MXtfyZgBuf2elFLhHFdfcgUDprriNANHma47zOLN8vdq+btJg+t5FeX6c1wllR5Y/szERkU2q2upczh44IiKyI6vVHE2ZvC6blSLD3H0O2wsRZw+PzZ6XtX/nfpy3f868pyXLKX5FqDBq2vMS5RotQo8lBcvyZzZhDOCIiMiOrKZ2mor6uqqDqO43SxOMV6s1qA1qpEZJL7LREPIKFk0D+QGvw4P2ov81rvsH4KsTowd0tQRJSaXpFeVmiMkYzSjXKBv29aEINzYsiXsibyIiqidFLZUe9nXZmGDcK00yqJEapeqfaWW4MJU5a031DDvPYUVfT+k8ex2Hl6iVYJOssprlCqNJiXKNstJhfeDUKf04Bo6IiMgW0zEafmN57r3Wf9ue46aAUs9hVRBgOmYozrEovq8jpCjnO2yQFPU1530agLRxDBy5qbPPldcYOPbAERER2WKayuXXixZ099mrFwLAgJRKwLyHJ86UNb/XIUPK6ZMBwh5HlB7jKK+Zk1ibi3KNsseyftjO8shpQMgAjoiIyBbTVC6/ICGokeoW4DlVp1SaNITiTFnzClTPvKn0c9BrtHUcbtsM+5o5ibUdUa7RoqZvU3xyfKOFRUyIiIhsMS0+ETRI368ohLPYihcbvWRxFtnwKxrjfKzpMHtFYoJEec1FLqphUsgl6Lk2i8RwXrj6YPI+57h6KcfAERHlQU7TPOpS1Peqev2mQ4HuN4De7gOP1zqWJ+45k7JyTSZ5HGH3VaT5qmxdn0Hj1GyOY3PbVkMjcNAh4YsJUfaZXjM5mG/RawwcAzgioqzjAP3iirOhyesmWXEF4mlyu4bchAlMg4Jam0Gv17aq5fH9oIFMr5kc3GjhRN5ERHmV4zQPCuD23vb1AMPeVtvcWdWKOi9fFjnn4Ot6BVAtpXjm+dyHndIhTGpoUFqpzbTTMM/hd2j+mV4zOZ5vkUVMiIiyrsjjaepd3O8tCzskwy8Qv3J7OsdkQ9jrMEzRmKAiMEGPR0mV9a3IWoXfoflmWkwpx9VL2QNHRJR1QYUtKF+qB92Lx59hvrf5YiMQz2LRjTDXYdgei6DeDr/HnT2clWqBXufIbVtu+DnLNxs9aH6FoTKMARwRUdblOM2DHJwNUbc5zfje5o/pTZaoAUpS3L57GhprSw0NSun1ezxqGnma1UqTlMWgP0l1nCbOIiZERHmQlYp/ZMZr0LwMAbQvv+9tvV+ftRSMqT5n0uAezIctphDn+c/Ce+tZLRAoNdwjVnstwjVa5CJFRXuvDLAKJRERUdpyULY6siI3JKOI0ugMW90xzHVRD+c/6aqSaQbE9ThVRbV6uJ4jiCWAE5HvAfgwgJdVdYrPejMBPAzgY6r6n0HbZQBHRESFVMRGVxFfU9zCBCSAnfL8SYor8LE5nUEt+7IVQARNGxJl+om0bwY53+tJpwF/vMf8vc/S9ZwBcU0j8AMApwfseAiArwK423BfRERE+VbE8YyskhpdmHMT9rqo5fzHMXYqznF8zrFOXmxcc3FO2+JVrbTrFfRPP1EdvPntO83iVm7v9cbv2nnv+X0SilEAp6r3A3glYLWLAdwG4GWTfREREeVeEQfds0pqdF7nRoYg8nUR9fwHBVq1Bndxz1dZXS1w5Dj3dWxcc3EGELVuw+15ad4MCjM/YK3vPb9PQol1HjgRaQFwNoA5AGYGrLsYwGIAGD9+fJyHRURElJ6izc02d5l7ylmeexXj5nXOagnmw5z/oIIp1Y3t6m1VgjsgRDEWj5TQOHpO4rzmTOcWq2XbYZ7nlOYcZmHf01ree36fhBL3NALfAHClqltppYFUdaWqtqpq6+jRo2M+LCIiIrKiiL2KcbN5zoK2FWbqCqDU2I7aizZg2x7i6DmJ85oL07NVay9l2PnpqjU0At1vuu8raA6zuKYZCPue1vLe8/skFOMqlCIyAcDP3IqYiMh2HEhWHgVgH4DFqrrOb5ssYkJERERkQZSCKXvbEakwRtC281o90K8Yi2mRk+ptuxUtqbWoidt+/AqmuL0um1VU81IVNONim0bAL4BzrPeD8nqsQklERJQ1ddxIKjTfOdTKKo1tr1RIrwqAftseOa6Y15DtKol+nzuTfUWZegEwm8fQZhVKt/3U8bQCXgGc0Rg4EfkxgFMAjBKRdgDXAGgEAFX9tsm2iYiIKCHORlKYsU+UD17jrrwmj48y/shzvFgOSr7XesPCdpETvzGxJvsKs051eqxX6qzXsSU1ltcvrddt/3VyI8oogFPVcyKse77JvoiIiCgmURtJttVJoysVUQqmRC2MkdeCEyY3LOIscmJzX2ELpvgFelko3R8liK2jG1FxFzEhIiKirEtz7qU45w+j6EUhggpjmGw7K0ymPEiyfL/JvsIWTBk5Nlzp/rgKooQ5vrDL457KIkNinUaAiIiIciDJXgWntHv/6kGc6W55nBbD5IZFkuX7TfblfK5XQZRKMOjXk5pmz1aUXt46mgScARwREVG9SzMVro4aXZQRpjcskgxaTfblfG5QqrLXY2neZIkSxIZ5XwuSrs0AjoiIqN6lOSlwmr1/VJ+SvGGRpYDBLxiMq5iKm6jnJGwQG/S+FmiMHAM4IiIiSi8VLq+FMCi/krphkXTAEFewaPMmS5znJOh9LVC6tvE8cHHgPHBERER1JEu9FES22J4zzk+c86XZ3HaS58TJc95Cj4nqMyCWeeCIiIiIjOWxEAZRkCTHd8bZu2SzxzLNMa8FStdmAEdEREREZFuSAUPcgZGtmyxpBlEFStfmPHBEREREeZLWnFwUTZJzxkWZLy1NSZ4TYOBn5d5rgWM/kb95C12wB46IiIgoLwpUSa/wkqzuWkvvUhpjT5M8J26flcduzW3QVo1FTIiIiIjyIs0iEJRtUQKyOIueZEUBPissYkJERESUd2nPyUXZFWWcWpxFT7JyTaVZMCVmHANHRERElBc2xzpVemH27gCgB9IxOaau+OIKbrJ0TeVlXGANGMARERER5YXNIhB+vTBUbHEFN1m6ppIumJIgBnBEREREeTFtUWmcko1KegVOMaMAcQU3WbqmbH5WMoZj4IiIiIjypAhzclF0NseWxVUNspZrKs4xc7Y+KxnDAI6IiIioHhVoYuPCi2P6iDiCm6jXFKfFqAlTKImIiIjqUYFTzBKVxMTqWRpb5ifqNZWX15UxRj1wIvI9AB8G8LKqTnF5/FwAV5Z/fQPA36vqYyb7JCIiIiJLCppilpikepCyNLYsSJRrKk+vK0NMe+B+AOB0n8e3AzhZVacB+DKAlYb7IyIiIiLKhqR6kIpaEr+orytmRgGcqt4P4BWfx3+rqq+Wf30YAN8NIiIiIiqGpHqQiloSv6ivK2ZJjoH7DICfez0oIotFZKOIbNy1a1eCh0VEREREVIOkepCSHq+YxLg+gOMwaySqarYBkQkAfuY2Bq5qnfcD+A8AJ6nqnqBttra26saNG42Oi4iIiIgoVs4xcECpBynPQUgRX1NOicgmVW11Lo+9B05EpgFYBeCsMMEbEREREVEuFLEHiZUhMy/WeeBEZDyAtQDOU9X/jnNfRERERESJK1olT1aGzDzTaQR+DOAUAKNEpB3ANQAaAUBVvw1gGYDDAfyHiADAfrduQCIiIiKiVGxdU+pd2tteGrs2d1mxArKoRo4tTYfgtpwywSiAU9VzAh6/EMCFJvsgIiIiIopFUvO45cncZe5j4FgZMjOSrEJJRERERJQdHO81WBHH9RVMrGPgiIiIiIgyi+O93BVtXF/BsAeOiIiIiOpTUvO4EVnEAI6IiIiI6tPcZaXxXdU43osyjgEcEREREdUnjveiHOIYOCIiIiKqXxzvRTnDHjgiIiIiIqKcYABHRERERESUEwzgiIiIiIiIcoIBHBERERERUU4wgCMiIiIiIsoJBnBEREREREQ5Iaqa9jEMIiK7ADyf9nG4GAVgd9oHUad47tPF858envt08fyni+c/PTz36eL5T0+Wzv07VXW0c2EmA7isEpGNqtqa9nHUI577dPH8p4fnPl08/+ni+U8Pz326eP7Tk4dzzxRKIiIiIiKinGAAR0RERERElBMM4KJZmfYB1DGe+3Tx/KeH5z5dPP/p4vlPD899unj+05P5c88xcERERERERDnBHjgiIiIiIqKcYABHRERERESUEwzgQhCR00XkaRF5RkSWpn08RSci40Tk1yKyTUSeFJHPl5cvF5EOEdlS/vehtI+1iETkORF5vHyON5aXHSYivxSRP5b/PzTt4ywiEZlcdX1vEZHXRORSXvvxEZHvicjLIvJE1TLP611Erir/LXhaROalc9TF4HHuV4jIUyKyVURuF5Hm8vIJItJV9Rn4dnpHXgwe59/zu4bXvj0e53511Xl/TkS2lJfz2rfMp52Zm+9+joELICJDAPw3gFMBtAN4BMA5qvqHVA+swETkKABHqeqjInIIgE0AFgBYBOANVf1aqgdYcCLyHIBWVd1dtewGAK+oalv5JsahqnplWsdYD8rfPR0ATgRwAXjtx0JEZgN4A8DNqjqlvMz1eheRYwD8GMAJAMYA+BWAv1TV3pQOP9c8zv1pAO5T1f0i8lUAKJ/7CQB+VlmPzHmc/+Vw+a7htW+X27l3PP6vAPaq6rW89u3zaWeej5x897MHLtgJAJ5R1WdVtRvATwCclfIxFZqqvqiqj5Z/fh3ANgAt6R5V3TsLwA/LP/8QpS86itdcAP+jqs+nfSBFpqr3A3jFsdjrej8LwE9U9c+quh3AMyj9jaAauJ17Vb1HVfeXf30YwNjED6xOeFz7XnjtW+R37kVEULph/eNED6qO+LQzc/PdzwAuWAuAHVW/t4PBRGLKd55mAPhdedE/llNrvsc0vtgogHtEZJOILC4vO0JVXwRKX3wA3pHa0dWPj2PgH3Be+8nxut759yBZnwbw86rfJ4rIZhH5LxF5X1oHVQfcvmt47SfnfQD+pKp/rFrGaz8mjnZmbr77GcAFE5dlzDtNgIiMAHAbgEtV9TUA/wfAuwFMB/AigH9N8fCKbJaqHgfggwAuKqd6UIJEZBiA+QB+Wl7Eaz8b+PcgISLyTwD2A7ilvOhFAONVdQaA/w3gVhF5e1rHV2Be3zW89pNzDgbevOO1HxOXdqbnqi7LUr3+GcAFawcwrur3sQB2pnQsdUNEGlH6UN2iqmsBQFX/pKq9qtoH4Dtg+kYsVHVn+f+XAdyO0nn+UzlnvJI7/nJ6R1gXPgjgUVX9E8BrPwVe1zv/HiRARD4F4MMAztXyQP1y6tKe8s+bAPwPgL9M7yiLyee7htd+AkRkKICFAFZXlvHaj4dbOxM5+u5nABfsEQCTRGRi+a74xwGsT/mYCq2c//1dANtU9etVy4+qWu1sAE84n0tmRORt5QG9EJG3ATgNpfO8HsCnyqt9CsAd6Rxh3RhwB5bXfuK8rvf1AD4uIgeJyEQAkwD8PoXjKywROR3AlQDmq+q+quWjy4V9ICLvQuncP5vOURaXz3cNr/1kfADAU6raXlnAa98+r3YmcvTdPzTNnedBuRLWPwK4G8AQAN9T1SdTPqyimwXgPACPV8roArgawDkiMh2lbuvnAHw2ncMrtCMA3F76bsNQALeq6i9E5BEAa0TkMwBeAPDRFI+x0ERkOEpVb6uv7xt47cdDRH4M4BQAo0SkHcA1ANrgcr2r6pMisgbAH1BK77uIVfhq53HurwJwEIBflr+HHlbVzwGYDeBaEdkPoBfA51Q1bAEOcuFx/k9x+67htW+X27lX1e9i8NhngNd+HLzambn57uc0AkRERERERDnBFEoiIiIiIqKcYABHRERERESUEwzgiIiIiIiIcoIBHBERERERUU4wgCMiIiIiIsoJBnBERJR7IvJG+f8JIvIJy9u+2vH7b21un4iIKAoGcEREVCQTAEQK4CqT5PoYEMCp6l9HPCYiIiJrGMAREVGRtAF4n4hsEZHLRGSIiKwQkUdEZKuIfBYAROQUEfm1iNwK4PHysnUisklEnhSRxeVlbQCaytu7pbys0tsn5W0/ISKPi8jHqrb9GxH5TxF5SkRukfKs1ERERKaGpn0AREREFi0F8AVV/TAAlAOxvao6U0QOAvCgiNxTXvcEAFNUdXv590+r6isi0gTgERG5TVWXisg/qup0l30tBDAdwLEARpWfc3/5sRkA3gtgJ4AHAcwCsOH/s3fn4VGVd//H33cmy2SdbJCEJCyyqUDYAohQBUFxqZVaiz6trba12rp20+qjj1r7q8vT1qXWpbZaaxf7qLVoq60CigoqCgXZVDaBJCTsSUgySSYz9++PM0kmyYQty2T5vK6LazLnnDnnO4mS+fC9z313/tsVEZH+Rh04ERHpy84Cvm6MWQOsADKAkcF9H4SEN4DrjTEfAe8D+SHHtWcm8Ky11m+t3Q28BUwJOXextTYArMEZ2ikiItJh6sCJiEhfZoDrrLWvtdhozCygutXzucB0a22NMWYp4D6Kc7enLuRrP/p9KyIinUQdOBER6UsOAckhz18DvmuMiQEwxowyxiSGeZ0HOBgMbycCp4Ts8zW+vpW3gYuD99kNAE4DPuiUdyEiItIO/YugiIj0JWuBhuBQyKeBh3CGL/4nOJHIXmB+mNf9G/iOMWYt8CnOMMpGTwBrjTH/sdZ+NWT734HpwEeABW6y1pYFA6CIiEiXMNbaSNcgIiIiIiIiR0FDKEVERERERHoJBTgREREREZFeQgFORER6DGPMv4wxl3X2sSIiIn2F7oETEZEOMcZUhTxNwJlC3x98fpW19s/dX5WIiEjfpAAnIiKdxhizHbjCWrs4zL5oa21D91fVu+j7JCIih6MhlCIi0iWMMbOMMcXGmB8bY8qA3xtj0owx/zTG7DXGHAx+nRfymqXGmCuCX19ujFlmjPlF8NjPjDHnHOexw4wxbxtjDhljFhtjHjHG/Kmduo9UY7ox5vfGmF3B/QtD9l1gjFljjKk0xmw1xpwd3L7dGDM35Lg7G69vjBlqjLHGmG8ZY3YCbwS3P2+MKTPGVARrHxPy+nhjzC+NMTuC+5cFt71ijLmu1ftZa4wJt3SCiIj0QgpwIiLSlbKBdGAIcCXO753fB58PBrzArw/z+mk467JlAv8LPBlcz+1Yj/0LziLbGcCdwNcOc80j1fhHnKGiY4CBwAMAxpipwDPAjUAqzsLe2w9zndZOB04C5gWf/wsYGbzGf4DQoai/ACYDp+J8f28CAsAfgEsbDzLGjAdygVePoQ4REenBtJC3iIh0pQBwh7W2LvjcC/ytcacx5mfAm4d5/Q5r7W+Dx/4BeBTIAsqO9lhjTCwwBZhjra0HlhljXm7vgtba/e3VaIzJAc4BMqy1B4OHvBV8/BbwlLV2UfB5yWHeVzh3WmurQ+p4KqSGO4GDxhgPcAj4JnCKtbbxGu8Gj3sJeNwYM9JauxknqP5f8H2LiEgfoA6ciIh0pb3W2trGJ8aYBGPMb4JD/yqBt4FUY4yrndc3BTVrbU3wy6RjPHYQcCBkG0BRewUfocb84LkOhnlpPrC1vfMehaaajDEuY8y9wWGYlTR38jKDf9zhrhUMys8BlxpjooD/wukYiohIH6EAJyIiXan1TFk/BEYD06y1KTjDDAHaGxbZGUqBdGNMQsi2/MMcf7gai4LnSg3zuiJgeDvnrMYZdtkoO8wxod+rrwAXAHMBDzA0pIZ9QO1hrvUH4KvAHKDGWvteO8eJiEgvpAAnIiLdKRlnGGW5MSYduKOrL2it3QGsBO40xsQaY6YD5x9PjdbaUpx70x4NTnYSY4xpDHhPAt8wxswxxkQZY3KNMScG960BLgkeXwhcdISyk3GWY9iPE/zuDqkhADwF3G+MGRTs1k03xsQF97+HM3T1l6j7JiLS5yjAiYhId3oQiMfpIr0P/LubrvtVYDpOIPp/wP/hBKRwjlTj1wAf8AmwB/gegLX2A+AbOJOaVODcGzck+Jr/wemYHQR+gjOpyuE8A+zAuY9uY7COUD8C1gEfAgeA+2j5O/0ZYBwQdqZNERHpvbQOnIiI9DvGmP8DPrHWdnkHMBKMMV8HrrTWzox0LSIi0rnUgRMRkT7PGDPFGDM8OLTxbJz7yxYe6XW9UfBev6uBJyJdi4iIdD4FOBER6Q+ygaVAFfAr4LvW2tURragLGGPmAXuB3Rx5mKaIiPRCGkIpIiIiIiLSS6gDJyIiIiIi0ktER7qAcDIzM+3QoUMjXYaIiIiIiEhErFq1ap+1dkDr7T0ywA0dOpSVK1dGugwREREREZGIMMbsCLddQyhFRERERER6CQU4ERERERGRXkIBTkREREREpJfokffAiYh0Fp/PR3FxMbW1tZEuRUT6MLfbTV5eHjExMZEuRUT6OAU4EenTiouLSU5OZujQoRhjIl2OiPRB1lr2799PcXExw4YNi3Q5ItLHaQiliPRptbW1ZGRkKLyJSJcxxpCRkaFOv4h0CwU4EenzFN5EpKvp7xmRXmjtc/DAWLgz1Xlc+1ykKzoqGkIpIiIiIiL9y9rn4B/Xg8/rPK8ocp4DFCyIXF1HQR04ERERERHpPwIBeP225vDWyOeFJXdFpqZjoAAnIhJi4eoSZtz7BsNufoUZ977BwtUlHTpfeXk5jz766DG/7txzz6W8vLxD1+4pnn76aa699trIFdDJQ2T0M+0BP9Pj9OCDD1JTUxPpMkSku/kboGQVvPsw/OUS+N+hULU7/LEVxd1a2vFQgBMRCVq4uoRbXlxHSbkXC5SUe7nlxXUdCnHtfdj3+/2Hfd2rr75KamrqcV9XghqHyFQUAbZ5iEwHQpx+pr2XApxIP+H3QdEH8M798KcvwX1D4bdnOF23fZvg5AsgPj38az153Vrq8dA9cCLSb/zkHxvYuKuy3f2rd5ZT7w+02Ob1+bnphbU8+8HOsK85eVAKd5w/pt1z3nzzzWzdupUJEyYQExNDUlISOTk5rFmzho0bNzJ//nyKioqora3lhhtu4MorrwRg6NChrFy5kqqqKs455xxmzpzJu+++S25uLi+99BLx8fFhr/fb3/6WJ554gvr6ekaMGMEf//hHEhIS2L17N9/5znfYtm0bAI899hinnnoqzzzzDL/4xS8wxlBQUMAf//jHsOd9/vnn+clPfoLL5cLj8fD2229TU1PD5ZdfzieffMJJJ53E9u3beeSRRygsLOT3v/8999xzDzk5OYwaNYq4uLh2v0cd8q+boWxd+/uLPwR/XcttPi+8dC2s+kP412SPg3PubfeU+pke+Wd6+eWXEx8fzyeffMKOHTv4/e9/zx/+8Afee+89pk2bxtNPPw3As88+y9133421lvPOO4/77rsPgKSkJK655hoWL15MWload999NzfddBM7d+7kwQcf5Atf+AJ+v5+bb76ZpUuXUldXxzXXXMNVV13F0qVLufPOO8nMzGT9+vVMnjyZP/3pTzz88MPs2rWL2bNnk5mZyZtvvklSUhJVVVUAvPDCC/zzn//k6aefPur6RaSHaKiD4pWw413YscwJb77gP9ZkjoaCL8OQGc6flBxn+9DPtbwHDiAmHubc3v31HyMFOBGRoNbh7Ujbj8a9997L+vXrWbNmDUuXLuW8885j/fr1TWtFPfXUU6Snp+P1epkyZQpf+tKXyMjIaHGOzZs38+yzz/Lb3/6WBQsW8Le//Y1LL7007PUuvPBCvv3tbwNw22238eSTT3Lddddx/fXXc/rpp/P3v/8dv99PVVUVGzZs4Gc/+xnLly8nMzOTAwcOtPs+7rrrLl577TVyc3ObhgE++uijpKWlsXbtWtavX8+ECRMAKC0t5Y477mDVqlV4PB5mz57NxIkTj/t72CGtw9uRth8F/UyP7md68OBB3njjDV5++WXOP/98li9fzu9+9zumTJnCmjVrGDhwID/+8Y9ZtWoVaWlpnHXWWSxcuJD58+dTXV3NrFmzuO+++/jiF7/IbbfdxqJFi9i4cSOXXXYZX/jCF3jyySfxeDx8+OGH1NXVMWPGDM466ywAVq9ezYYNGxg0aBAzZsxg+fLlXH/99dx///28+eabZGZmHvHnfKT6G783IhIB9TXOP9DtWA7bl7f8x7qssTDxazB0Bgw+FZIGhD9H40QlS+5yhk168pzw1sMnMAEFOBHpRw7XKQOYce/OlHd9AAAgAElEQVQblJR722zPTY3n/66a3ik1TJ06tcVCv7/61a/4+9//DkBRURGbN29u82F/2LBhTR8WJ0+ezPbt29s9//r167ntttsoLy+nqqqKefPmAfDGG2/wzDPPADR1XJ555hkuuuiipg+z6entDCcBZsyYweWXX86CBQu48MILAVi2bBk33HADAGPHjqWgoACAFStWMGvWLAYMcH5pXnzxxWzatOnovkHH6jCdMsC5562iqO12Tz5845VOKUE/0/DOP/98jDGMGzeOrKwsxo0bB8CYMWPYvn07O3bsaHHOr371q7z99tvMnz+f2NhYzj77bADGjRtHXFwcMTExjBs3rul79frrr7N27VpeeOEFACoqKti8eTOxsbFMnTqVvDxnGNSECRPYvn07M2fOPGy9x1q/ApxIN6qrgqL3nQ7b9uXO/WwBH5goZ9TElCuCgW06JLT/914bBQt6RWBrTQFORCToxnmjueXFdXh9zfcyxce4uHHe6E67RmJiYtPXS5cuZfHixbz33nskJCQwa9assAsBhw5Vc7lceL1tQ2ajyy+/nIULFzJ+/Hiefvppli5d2u6x1tqjXrvq8ccfZ8WKFbzyyitMmDCBNWvWYK1t9/gesybWnNu7fIiMfqbhNb7HqKioFu83KiqKhoYGoqPb/wgSExPTdL3Q1ze+Fpz3+vDDDzcF2kZLly5t8/1tfM3h3lPrn9OR6heRLlRbATvfh+3LnC7brjVg/WBcMGgiTL8ahsyEwdPA7Yl0td1Ok5iIiATNn5jLPReOIzc1HoPTebvnwnHMn5h73OdMTk7m0KFDYfdVVFSQlpZGQkICn3zyCe+///5xX6fRoUOHyMnJwefz8ec//7lp+5w5c3jssccAZ7KNyspK5syZw3PPPcf+/fsBDjvcbuvWrUybNo277rqLzMxMioqKmDlzJs8950wGsnHjRtatc+5FmzZtGkuXLmX//v34fD6ef/75Dr+v41awAM7/ldNxwziP5/+qQ//iqp9p5/xMp02bxltvvcW+ffvw+/08++yznH766Uf9+nnz5vHYY4/h8/kA2LRpE9XV1Yd9TeufXVZWFh9//DGBQKCpayoiEVBzAD7+J/z7FvjNac6kI39ZAO8/BlExMPN7cOmLcPNO+PYSOPMuGHVWvwxvoA6ciEgL8yfmdiiwtZaRkcGMGTMYO3Ys8fHxZGVlNe07++yzefzxxykoKGD06NGccsopHb7eT3/6U6ZNm8aQIUMYN25c04fVhx56iCuvvJInn3wSl8vFY489xvTp07n11ls5/fTTcblcTJw4sd3JGW688UY2b96MtZY5c+Ywfvx4Ro4cyWWXXUZBQQETJ06koKAAj8dDTk4Od955J9OnTycnJ4dJkyYdcYbGLtXJQ2T0M+2cn2lOTg733HMPs2fPxlrLueeeywUXXHDUr7/iiivYvn07kyZNwlrLgAEDWLhw4WFfc+WVV3LOOeeQk5PDm2++yb333svnP/958vPzGTt2bNOEJiLSxar2Op21xnvY9mxwtke7IW8KnHaTMyQyb4ozakJaMIcbLhEphYWFduXKlZEuQ0T6gI8//piTTjop0mX0SX6/H5/Ph9vtZuvWrcyZM4dNmzYRGxsb6dLkOOln2jH6+0akHZWlwbC2zLmPbd+nzvaYBMif5swOOXQG5E6G6C6atbgXMsasstYWtt6uDpyIiByXmpoaZs+ejc/nw1rLY489pg/6vZx+piLSKcqLQgLbcjjgLHdCbDIMPgUm/JdzD9ugCeCKiWytvZACnIhIL3TNNdewfPnyFttuuOEGvvGNb3TovD/72c/a3N/05S9/mVtvvbXNscnJyWi0ROfpjT/TYzm3iPRR1sLBz5yhkI1DIiuCa6e6U2HIqVD4TafLll0ALsWPjtIQShHp0z7++GNOPPHEnjMrooj0SdbapgXQRfo0a2H/lubu2vblcGiXsy8hwwlsQ2Y6QyIHjoEozZl4vDSEUkT6Jbfbzf79+8nIyFCIE5EuYa1l//79uN3uSJci0vkCAdj7Sct72Kr3OPuSsprvXxsyAwacCPpd2+UU4ESkT8vLy6O4uJi9e/dGuhQR6cPcbnfT4uEivVrAD7vXBxfNDgY2b3BJkpRcOGFWMLDNhIzhCmwRoAAnIn1aTEwMw4YNi3QZIiIiPZO/Aco+ar6Hbed7zkLaAKlDYPQ5TndtyKmQNlSBrQdQgBMRERER6S8a6mHX6uZ12HaugPrgAvfpw+HkC5rvYfOoq9wTKcCJiIiIiPRVvlooWdV8D1vxh+CrcfYNOBEKFjTfw5acHdla5agowImIiIiI9BX1NVD8QfAetuVOYPPXAQayxsDErzUHtsTMSFcrx0EBTkRERESkt6o7BEUrmu9hK/kPBHxgopx116Z+2wlrg0+BhPRIVyudQAFORERERKS38JbDzvdhR3CGyF1rwPohKhoGTYTpVzv3sA2eBm5PpKuVLtChAGeMORt4CHABv7PW3ttq/yzgJeCz4KYXrbV3deSaIiIiIiL9Rs0BJ6g13sNWtg6w4IqF3Mkw8/vOkMi8qRCXFOlqpRscd4AzxriAR4AzgWLgQ2PMy9baja0Ofcda+/kO1CgiIiIi0j9U7QmGteCQyD3Bj9bRbsibAqf/OBjYpkBMfGRrlYjoSAduKrDFWrsNwBjzV+ACoHWAExERERGRcCpLm7trO5bDvk3O9pgEyJ8GYy90hkTmToLouMjWKj1CRwJcLlAU8rwYmBbmuOnGmI+AXcCPrLUbwp3MGHMlcCXA4MGDO1CWiIiIiEgPVb4z2F1b5jweDN5pFJfiTDQy4avOpCODJoArJrK1So/UkQAXbhl22+r5f4Ah1toqY8y5wEJgZLiTWWufAJ4AKCwsbH0eEREREZHexVo4sC3kHrblULHT2edOhSGnwpQrnCGR2QUQ5YpsvdIrdCTAFQP5Ic/zcLpsTay1lSFfv2qMedQYk2mt3deB64qIiIiI9DzWwr7Nzd21He/CoeDH44RMJ7Cdeq3zOHAMREVFtl7plToS4D4ERhpjhgElwCXAV0IPMMZkA7uttdYYMxWIAvZ34JoiIiIiIj1DIAB7Pwm5h+1dqN7j7EvKcoZCDp3h3MM2YDSYcAPYRI7NcQc4a22DMeZa4DWcZQSestZuMMZ8J7j/ceAi4LvGmAbAC1xirdXwSBERERHpfQJ+2L2+eYbIHe+C94CzLyUPhs92QtuQGZAxXIFNuoTpiXmqsLDQrly5MtJliIiIiEh/5m+A0o+CYW057HgP6iqcfalDYOjM5i5b6hAFNulUxphV1trC1ts7tJC3iIiIiEif0VAPu1Y338NWtALqq5x9GSNgzPxgaDsVPHmRrVX6LQU4EREREemffLVQsrJ5SGTRB9DgdfYNOAkKLg7ewzYDkrMjW6tIkAKciIiIiPQP9TVQ/EFzYCteCf46wEDWWJh8WfAetlMhMTPS1YqEpQAnIiIiIn1T3SHYucIZErnjXSj5DwR8YKIgZzxM/XYwsE2H+LRIVytyVBTgRERERKRv8JbDzveb72Er/QisH6KiYdBEmH6Ncw9b/jRwp0S6WpHjogAnIiIiIj3P2udgyV1QUexMGDLndihY0PKYmgPBNdiCQyLL1gEWXLGQWwif+4EzHDJ/GsQmRuRtiHQ2BTgRERER6VnWPgf/uB58wQlFKoqc57WVkJjRHNj2bHT2R7shbwrMutkZEplXCDHxkatfpAspwImIiIhI1wr4IdAAfp9zD5q/Ifjoc7a33vfarc3hrZHPC6/+0Pk6JhEGT4OxX3ICW+4kiI7r/vclEgEKcCIiInJ8jmaImxy7QCAk3Pic8NMUbnwhYad18DmKfe2GKH/LazbuCzS0DVtN5woTxNrbh+28788VS5wJSFwxnXdOkV5EAU5ERESOXXtD3KB7Q1wgEBIYfC0DSrvh41iDSesQdYTu0ZH2tbl2477gNhvovu+fcTkTfLhiQh5jwBXtPI+KCbMvBmITwuyLbntci22t97nCnz8qGl66Gqr3tq3Xk+8MjxTpxxTgRERE5NgtuSv8ELdXfgQHth1F9+hYOkuhwafVObo17ES1Hzhc4cJOMLzExHc80LR7/phW+4LnaRHEwl278XlU933/jsW8u1v+AwE438c5t0euJpEeQgFOREREjl5FCWxZ5HTcwqmrgKX3tBN2wgWOVoGmRdjpjEDTuit0FPvCBayeHHb6osYuroboirShACciIiLt8/ucdbW2LILNi2HPBme7cTnra7XmyYMb1insSMcVLFBgEwlDAU5ERERaqtwFmxc5oW3bW1BX6XShhkyHM38KI8901tsKO8TtDoU3EZEupAAnIiLS3/l9UPQBbH4dtiyG3eud7Sm5MPZCGHEmnHA6xCU3v2bgSc6jhriJiHQrBTgREZH+qLLUCWubX4dtS4NdtmgYPB3m/gRGnuWENGPaP4eGuImIdDsFOBERkf7A3wDFHzQPjSxb52xPHgRj5ge7bLPAnRLJKkVE5AgU4ERERPqqQ2XBLtsi2PYm1FY4k48Mng5z73RCW9aYw3fZRESkR1GAExER6Sv8DVCy0hkWuXkRlK11tidlw0lfcCYfOWEWuD2RrFJERDpAAU5ERKQ3q9rTfC/b1jeau2z505wZIUeeCVlj1WUTEekjFOBERER6k4AfilcG12VbBKVrnO1JWXDi+TByLpwwG+JTI1uniIh0CQU4ERGRnq5qr9Nl27LI6bJ5D4KJcrpsZ/yP02XLLlCXTUSkH1CAExER6WkCfij5T3BdtkWwa7WzPXEgjD4XRsyF4bMhPi2ydYqISLdTgBMREekJqvfBliXN97J5DzhdtrwpcMZtzoyR2QUQFRXpSkVEJIIU4ERERCIh4Hc6a43rspX8B7CQOABGzQvOGDkbEtIjXamIiPQgCnAiIiLdpXo/bF3ihLatS6BmP2CcLtvs/w7eyzZeXTYREWmXApyIiEhXCQSgNNhl27wISlYBFhIynSGRI8+E4WeoyyYiIkdNAU5ERKQz1Rxw7mHb/LpzT1vNPsBA7mSYdYszzX/ORHXZRETkuCjAiYiIdEQg4KzF1riYdskqsAFIyIDhc2DkWU6XLTEj0pWKiEgfoAAnIiJyrBq7bFsWO3+q9+J02SbBaTc5QyMHTYQoV6QrFRGRPkYBTkRE5EgCAShb2zxjZPGHTpctPs1Zk23EmTBiDiRmRrpSERHp4xTgREREwvEehK1vBkPbYqje42wfNBFOu9EJbbmT1GUTEZFupQAnIiICYG1zl21zY5fND+5Up7s28iznnrakAZGuVERE+jEFOBER6b+85bDtTdi82BkaWbXb2Z4zAT73Aye05U5Wl01ERHoMBTgREek/rIWydU5Y27wYilY0d9mGn+FMPjJiLiQNjHSlIiIiYSnAiYhI31ZbAduWNq/LdqjU2Z4zHmZ+3wltuYXg0q9EERHp+fTbSkRE+hZrYfeGYJdtkdNlCzRAnAeGz3aGRY6YC8lZka5URETkmCnAiYhI71db6XTZGodGHtrlbM8eB6de74S2vCnqsomISK+n32QiItL7WAt7Pg4Oi1wMO98L6bLNCq7LNhdSciJdqYiISKfqUIAzxpwNPAS4gN9Za+9t57gpwPvAxdbaFzpyTRER6afqDsG2t5pDW2WJsz1rLJx6nRPa8qeCKyaydYqIiHSh4w5wxhgX8AhwJlAMfGiMedlauzHMcfcBr3WkUBER6Weshb2fBNdlex12vg8BH8QmO122WTcHu2yDIl2piIhIt+lIB24qsMVauw3AGPNX4AJgY6vjrgP+BkzpwLVERKQ/qKuCz95yQtuWxVBR5GwfOAamX+3cy5Y/TV02ERHptzoS4HKBopDnxcC00AOMMbnAF4EzOEKAM8ZcCVwJMHjw4A6UJSIivYa1sPfT5hkjd7zb3GU74XQ47UfO0EhPbqQrFRER6RE6EuBMmG221fMHgR9ba/3GhDs85IXWPgE8AVBYWNj6PCIi0lfUV8NnbzvDIjcvhoqdzvaBJ8Mp33XWZcs/BaJjI1uniIhID9SRAFcM5Ic8zwN2tTqmEPhrMLxlAucaYxqstQs7cF0REelNrIV9m4NdttedLpu/HmKT4IRZ8LkfOPeypeYf6UwiIiL9XkcC3IfASGPMMKAEuAT4SugB1tphjV8bY54G/qnwJiLSD9RXw2fvNIe28mCXbcCJMO0qZ1jk4OnqsomIiByj4w5w1toGY8y1OLNLuoCnrLUbjDHfCe5/vJNqFBGRns5a2L8lOPnIIti+HPx1EJPo3Ms243vO0MhU3eMsIiLSER1aB85a+yrwaqttYYObtfbyjlxLRER6mPoa2L4suC7bIji43dmeORqmftsJbIOnQ3RcRMsUERHpSzoU4EREpJ/Zv7V5Xbbty4JdtgQYdnrzYtppQyJdpYiISJ+lACciIu3zeYNdtmBoO/iZsz1jJEy5AkbOhcGnQow7snWKiIgco4WrS/j5a5+yq9zLoNR4bpw3mvkTe/6yNQpwIiLS0v6tziLamxfB9negoRai42HYaTD9GmdoZNrQSFcpIiJy3BauLuHmF9dS6wsAUFLu5ZYX1wH0+BCnACci0t/5vM6kI42LaR/Y6mzPGAGTv+F02YbMVJdNRER6BWstlbUNlFXUUlrhpbSiltKKWspCvt66p6rNAtZen5+fv/apApyIiETQ2udgyV1QUQyePJhzOxQsgAOfBbtsrzvT/Td4IdrtdNmmfccJbeknRLp6ERGRFqy1VHh9wSDmDQaz2qbHXRVeyipqqan3t3idMTAgKY4cj5sRA5LYsqcq7Pl3lXu74210iAKciEhftfY5+Mf1TocNoKIIFn4XXrsVqvc429JPgMmXOZOPDJ0BMfGRq1dERPo1ay0Ha3xOMCuvpbSyuWvWGNJKK7xNwx4bRRkYmOwmJ9XNidnJzBo1kByPm2yPm0GpbrI98QxMjiPGFdX0mhn3vkFJmLA2KLXn/x5UgBMR6WsCAWeykX/f3BzemvY1QF0lnPO/MGIuZAyPTI0iItKvBAKWAzX1Tpes3EtZZW1IMGse2ljf0DKcuaIMWclx5KTGc/KgFOacODAYzOLJ9rjJ8bgZkBRHdEg4Oxo3zhvNLS+uw+tr7tTFx7i4cd7oTnm/XUkBTkSkNwv4Yd9mKP2o+U/ZWiektaehDqZd1X01iohInxYIWPZV1zlds8Z7zSqD4ay8ltJKL7sr6qj3twxnMS5DVooTwgryUpk3xk12SnPXLMfjJjMpDleU6fSaG+9z0yyUIiLSdRrqYe/HrcLaeuf+NXBmisweBwUXQ854eOOnULW77Xk8ed1bt4iI9Fr+gGVfVZ3TNWu81yzYPSstdzpnuytraQi0nBIk1hVFdnAY46TBaU7XzNPcNcv2uMlMjCOqC8LZ0Zo/MbdXBLbWFOBERHoinxd2b4DSNc1hbc/H4K939scmOyGt8JvOY854Z9ZIV8hf69FxLe+BA+cetzm3d+97ERGRHqnBH2BvVR27ypuHMobea1ZWUcvuQ3X4W4WzuOiophA2dVh6MJw1d82yPW7SE2IjGs76MgU4EZFIqzsEZetadtb2fgo2OC4/Pt0JaKdc3RzW0oZB1BHG+xcscB7DzUIpIiJ9ms8fYM+huqYuWWgwa3y+51AtrbIZ7piopk7ZKcMzyPG4yQkJZjmeeNISYjBG4SxSFOBERLpTzQHnHrXQsLZ/KzSuRpOU7QS0Ez/fHNY8ec78x8ejYIECm4hIH1PfEGB3ZctOWeuv91bVYVuFs4RYV1Mgmzkys03XLMfjxhOvcNbTKcCJiHSVqj3BkBYyDLJ8Z/N+z2DIKYCCS4JhrQCSsyNXr4iIRFytz98UzsJ1zUoratlXVdfmdclx0U33nI3OTm7TNctJdZMcF61w1gcowImIdJS1UFnSsqtW+hEcKm0+Jn045BZC4beaO2sJ6ZGrWUREul2tz3/YrllZRS37q+vbvC7FHU1OcFjj2NwUslNads2yPW6S3TEReEcSCQpwIiLHwlpnjbXWYa1mv7PfREHmaBh2enNQyx4H7pTI1i0iIl2qpr6hRZesrMLLrlbPD9b42rwuNSGG7OBU+uPzU8lJadk1y05xkxinj+zSTP81iIi0J+waa+ugrsLZHxUDA0+C0ecGw9oEyBoDsQmRrVtERDpVVV2Ds7ZZY9esvJayypbDGiu8bcNZemKss66Zx83kIalOFy0Y1nJSna/jY10ReEfSmynAiYhAcI21T1qGtd3rwVfj7I+Oh+yxUPDl5s7agJMgOjaydYuISIdU1vpads0ap9SvDC5IXV7LobqGNq/LTIol2+MmPz2haSr9HI+b7JR4BqW6yUpx445ROJPOpwAnIv2Pzwu7N7ZaY21jqzXWCmDy5SFrrI1sucaaiIh0qYWrS/j5a5+yq9zLoNR4bpw3+pgWXbbWUultoDS0U9Y4pX7IJCFVrcKZMZCZFEeOx82wzEROHZ4ZEs7cDEqNZ2BKHHHRCmcSGfo0IiJ9W90hKFvfao21T0LWWEsLrrH23eZhkEezxpqIiHSZhatLuOXFdXh9zt/VJeVebnlxHQDzJ+ZiraW8xhcMYyFds4qWQxtr6v0tzmsMDEyOI9sTz8iBSXxuZGZwEpD44JT6bgYmu4mN1u8A6bmMbb1ARA9QWFhoV65cGekyRKS38R6E0tZrrG2haY21xIEwaEJzVy1nPHjyj3+NNRER6TCfP0BNnZ+q+gZq6hqorvdzxR8+ZF9V29kYY6OjyE2Np7TCS60v0GJflIGsxvvLgjM2tphG3+NmQHIcMS6FM+kdjDGrrLWFrberAycivVPVnmBYC11jbUfzfk++E9AKFjSHNa2xJiJy3Ky11PsDVNf5qa5roKbeT3V9AzV1wcf6Bqrr/C0eq1o9r673U9PqtfX+wJEvHlTfEGDMoBTmnjSwRdcsxxNPZlIs0Qpn0g8owIlIz2YtVO4Ks8baruZj0k+A3ElQ+I3gtP3jITEjcjWLiESYtZZaX+CIAau63gljrQOWc3xIUAs+NgSOfuRWQqyLhNhoEuOCj7EuPPEx5Ka6m54nxAUfQ4+Lc3HTC2vDduByU+P59Vcmdea3SqTXUYATkZ4j7Bpra6Fmn7PfREHmKBj2uVZrrHkiW7eISAcEAhavr3U3q214qqprCNvNqq53nje9Nvh4tFnLGEiMjSYh1kViXPAxNpqMxFjy0xPaBqym4BVNQpxzbGLwsfF5fIyLqKjjH55+23knt7gHDiA+xsWN80Yf9zlF+goFOBGJjIDfuT+tdVhrs8ba2c7EIjnjg2usJUa2bhHp1/wB64SlMAGrMTw1Ba1WAau97lfriTYOJ8pAYpjwlJXsJiEzfDcrNHQlNT1vfr07JgrTw+4FbpxtsiOzUIr0VQpwItL1/L62a6yVrQtZY80NWWNh3EXNnbWBJ0F0XGTrFpFercEfOGJ4CnsvVztdsOr6hjYTZxxOdJQJhq3moYKJcdEMSo0N081qO5ywKaiFdMbionte2Ooq8yfmKrCJhKEAJyKdy1cLeza0WhB7Q8gaa0mQXQCTLmsOa5mjtMaaSC/U0XW6QtU3BNp2repCgtYRhheG637VNxx92IqNjgobnjISY5uHFYYML2zsXiXEukiKiw4bwjQVvYh0BX1iEpHjV1cFu1utsbbn4+Y11typTkCb9p3mNdbST9AaayJ9wMLVJdz84tqmjlRJuZebXviID7fv58QcT5sQFnboYMg+n//oJ8dwx0S1GkIYTVJctDOMMK5twGoMXklx0WGHFybEujS1vIj0GgpwInJ0vOVQ1mqNtX2baV5jbYAT0Ead3dxZSx2sNdZEejF/wFJWWUvRgRqKD3opPug8Fh2oYeX2g/hbrSVb77f8eUURUNS0rXEmwqSQ0JR6hJkIww0dbHxMiI3G1YHJMUREejsFOBFpq2pv8D61kLB2cHvz/pQ8J6CNvajlGmsKayK9ij9g2XOolqIDzeGs+GCN87y8htLy2hbTxhsDWclu8tLi24S3pmOAFbfO6ZSZCEVEpC0FOJH+7GjWWEsb5nTWQu9ZS8yMXM0ictQCAcueQ3Vhw1nxQS+7yr1thi4OTI4jLy2eiflpfGF8PHlpCeSlOY+DUt3ERbsAmHHvG5SUe9tcc1BqPAOT3d3y/kRE+iMFOJH+wlqni9Y6rDWusYZxJhMZOrPlGmvxqZGsWkQOw1rL3kN1FB1s2UFzHr2UHPRS7285kUdmkhPQCvJSOXdcTlM4y0uLJzc1HneM66iufeO80VqnS0QkAhTgRPqigB/2bw2GtDXB4ZBrobZxjbVoGHBSy/vVssdqjTWRHsZay76q+hahrCgkqJUc9FLXaqbFjMRY8tLiOXlQCmeNyWoKZ/lpCeSmxhMfe3QB7Ui0TpeISGQY284Y9kgqLCy0K1eujHQZIr2D3wd7Pw2zxlq1s98V54SzxqCWM94JbzEa4iQSadZaDlTXN4Wz4oM1IQHNed563bG0hBjy0hLITw8d3hgMaGnxJMTq32ZFRPoCY8wqa21h6+36W16kN/HVwp6NYdZYq3P2xyRCTgFM+lqrNdZiIlu3SD9lraW8xtduOCs+6KWm3t/iNZ74GPLS4hkxIIlZowY0DXHMT3cCWlKcfnWLiPRn+i0gEmlrn4Mld0FFMXjyYM7tULAA6quhrNUaa3s/hkCD8zq3J7jG2pXOJCM544NrrHXO8CgROTJrLZXehhbDGlvfh1ZV19DiNclx0eSlJzA0I5GZI5yAlp8evActLZ4Ut/7BRURE2qchlCKRtPY5+Mf14AuZyc24nDXVqnbTtMZaQiYMmtByGGTqEE3bL9INKmt9FB9o7KC1DGfFB2o41CqgJcVFt5gYJDSg5aUl4IlXQBMRkSPTEEqRnmjJXS3DG4D1Q205zLo5ZI21HIU1kS5SVdfQPL1+6+n2D9ZQWdsyoCXEusgPhrOpQ9Pa3I/miY/B6AgDwWQAACAASURBVP9XERHpIgpwIpFycAdUFIXf11DnBDgR6bDqugZKyr0UHWg5vLFx2GN5ja/F8e6YqKaANnlIWpsOWlqCApqIiESOApxIdwsEYOWTsOgOwNA0TDKUJ6+7qxLptbz1/rD3njUGtAPV9S2Oj4uOagpj4/NSW4SzvLR4MhJjFdBERKTH6lCAM8acDTwEuIDfWWvvbbX/AuCnQABoAL5nrV3WkWuK9Gr7t8LL18GO5TB8Dow8E5b8pOUwyph4ZyITEQGg1udvN5yVHKxhX1XLgBbrimqaEGTMIE+b6fYHJMUpoImISK913AHOGOMCHgHOBIqBD40xL1trN4YctgR42VprjTEFwHPAiR0pWKRXCvhhxeOw5KfgioULHoUJX3Hua0vICD8LpUg/Udfgp6Rpav22k4XsPVTX4vgYlyE31Qllc0/KCumgOdsGJMURFaWAJiIifVNHOnBTgS3W2m0Axpi/AhcATQHOWlsVcnwiYceKifRxezfBS9dA8Qcw6hz4/AOQktO8v2CBApv0afUNAXaVe8Pef1Z8sIbdlS0DWnSUYVCqE8hmjx7g3I8W0kUbmOzGpYAmIiL9VEcCXC4QOgNDMTCt9UHGmC8C9wADgfPaO5kx5krgSoDBgwd3oCyRHsLfAO/+CpbeC7EJcOHvYNxFmk1S+hyfP0BpeW3YcFZ80EtZZS2hK9a4ogw5Hjd5afGcNnJAm+n2s1IU0ERERNrTkQAX7rdrmw6btfbvwN+NMafh3A83N9zJrLVPAE+Asw5cB+oSibzdG2Dh1VC6Bk6+AM79BSQNjHRVIselwR+gtKI27PDGkoNeSiu8BEL+1o4ykONx7kE7dXhmi+GN+enxZKe4iXZFRe4NiYiI9GIdCXDFQH7I8zxgV3sHW2vfNsYMN8ZkWmv3deC6Ij1XQz0sewDe/jnEp8KX/wBj5ke6KunHFq4u4eevfcquci+DUuO5cd5o5k/MbXGMP2Apq6yl+ED4DlppRS3+kIRmDGSnOB20acPSW8zgmJ+eQLbHTYwCmoiISJfoSID7EBhpjBkGlACXAF8JPcAYMwLYGpzEZBIQC+zvwDVFeq5da5x73Xavh3EL4Ox7ITEj0lVJP7ZwdQm3vLgOr88PQEm5lxtf+IjXNpSR7I5umjRkV7mXhkDLgQ9ZKXHkpSVQOCQtZIij00HL8cQTG62AJiIiEgnHHeCstQ3GmGuB13CWEXjKWrvBGPOd4P7HgS8BXzfG+AAvcLG1VsMjpW9pqIO37oNlD0LiALjkWTjx3EhXJf1Erc/P7spayipqKWv1uPjj3fj8Lf/K9fkt/1pfxoDkOPLT4pmQn8rnC3JadNByPG7cMa4IvSMRERE5HNMT81RhYaFduXJlpMsQObLilc69bvs+hQmXwryfOUMnRTrIWkult8EJY5W1lFV4Kauoo6zSGwxpdZRVeDlY42vz2sRYF1keN9v2Voc9twE+u7fdOaVERESkBzDGrLLWFrbe3qGFvEX6LZ8X3vh/8P6jkDwILv0bjAg7P49IG/6AZV9VHWUVtZRW1DodtMbuWUgHrXHoY6iMxFiyPW4GedxMGpxKdoqbLI+bHI+b7BQ32R43ye4YAGbc+wYl5d425xiUGt/l71FERES6hgKcyLHa8S68dC0c2AqF34S5PwF3SqSrkh6icUhjUzALCWmNj3sO1bWYFASctc+yggHs5EEpnHHiwKZAlh0MZwNT4oiLPvqhjTfOG93iHjiA+BgXN84b3WnvV0RERLqXApzI0aqrgiV3wQdPQOpg+PrLcMLpka5KuknokMbSCm+LQFYa7Jztrqxtd0hjYxA7dXgm2Z44sj3OdPqNIS0jMZaoTl77rHG2ySPNQikiIiK9hwKcyNHY9ha8fB2U74RpV8Gc2yE2MdJVSSdpHNLYGMTKKryUVdYFw5mX3ZV1lFZ4qfUF2rw2MymWrOCU+pOHpJHjcTd10hq/bhzSGAnzJ+YqsImIiPQhCnAih1NbCYv+B1Y9DRkj4Bv/giHTI12VHINanz/sDI2hX++tajukMcZlGJjshLCTB6Uw58SBZAcDWWMwO9YhjSIiIiIdpQAn0p7Ni+Ef18OhUjj1epj93xCjyR96CmstFV5f2EAW+lgeZkhjUlx0031lM0dmNk8EEnLPWXpC5w9pFBEREekoBTiR1rwH4bVbYc2fYcCJsOCPkDc50lX1K/6AZe+hupDp82spraxld6twFn5IYxzZnjjy0uIpHJoWvMcsvsWEIElx+qtPREREeid9ihEJ9ckr8M/vQ/U+OO1G5090XKSr6lMahzS2npkxNKTtOVRLqxGNxLiCszSmuBmb62HuSVktZmjM9rgZmOwmNjoqMm9MREREpBsowIkAVO+Hf90I6/8GWePgq89DzvhIV9WrNA5pLA12x3aHCWmlFbVUeNsOaUyOi25ay2zkwMzmiUA0pFFERESkBQU46d+shQ1/h1dvhNoKmH0rzPw+uCI3a2BP1OAPsDe48HTTtPkh9541bqtraDmk0RjISIwjx+MmLy2BKUPT20wEoiGNIiIiIkdPn5qk/zq0G179IXz8Dxg0ES74B2SdHOmqup233h9yX5mXsoq64DT6tZRVOl/vPVTXZkhjrCuKLE9c05DGM0/OCgazeLI9cc4sjRrSKCIiItKpFOCk/7EW1j4H//4x1NfA3J/A9GvB1bf+d7DWUl7jO+z0+WWV7QxpdEc3DV8cNXBAi3vNGrtn6YmxGKMhjSIiIiLdqW99YhU5kspdziQlm/4NeVPhgkdgwKiIlrRwdQk/f+1TdpV7GZQaz43zRh9x4eXGIY2lFW1nZgydFCTckMbMJKdrNjgjganD0ltMAtL4daKGNIqIiIj0SPqUJv2DtbD6j87yAH4fzLsHpl0FUZFdhHnh6hJueXEdXp8fgJJyLze/uJa9h+oYk5vSPAFIq5B2uCGNOSnxFOSlMm9M24lABibHEePSkEYRERGR3koBTvq+8p3w8vWw7U0YMhMueBjST4h0VQD8/LVPm8Jbo1pfgJ+9+nGLbcnu6KZJP0ZlJTtfe5onAsnxxJOWEKMhjSIiIiJ9nAKc9F2BAKx6Chbd4Tw/75cw+ZsQ1TM6UFv2HKKk3Nvu/r9cMY0sDWkUERERkRD6VCh904FtTtdt+ztwwmz4wq8gdXCkqwJgx/5qHlq8mYVrSjCADXNMbmo8p47I7O7SRERERKSHU4CTviXghxW/gSV3gSsWvvBrmHipM3tHhJWUe3l4yWaeX1VMjMtwxedOYHB6PD975ZMWwyjjY1zcOG90BCsVERERkZ5KAU76jr2b4OVroWgFjJwH5z8IKYMiXRW7K2t55M0t/PWDIgC+dsoQrp41nIEpbgCS4mKOeRZKEREREemfFOCk9/M3wHsPw5v3QEw8fPEJKFgQ8a7bvqo6Hl+6lT++vwN/wPLlwnyuO2MEg1LjWxw3f2KuApuIiIiIHBUFOOnddm+El66GXavhpPPh3F9CclZESyqvqec3b2/jD+9up9bn58JJeVx/xkgGZyREtC4RERER6f0U4KR38vtg2QPw1v+C2wNffhpOnh/RrltlrY8n3/mMp5Z9RlV9A+cXDOKGuSMZPiApYjWJiIiISN+iACe9T+lHsPAa2L0Oxl4E59wHiZGbsbG6roGn393OE29vo8Lr4+wx2Xz/zFGMzk6OWE0iIiIi0jcpwEnv0VDndNyWPeAEtkv+AieeF7Fyan1+/vT+Dh5bupX91fWcceJAfnDmKMbmeiJWk4iIiIj0bQpw0jsUr3Luddv7CYz/Cpx9N8SnRaSUugY/f/2giEfe3MKeQ3XMHJHJD84axaTBkalHRERERPoPBTjp2XxeePNueO/XkJwDX30BRp4ZmVL8AV5YVczDSzazq6KWqUPTefi/JjLthIyI1CMiIiIi/Y8CnPRcO9+Hl66B/Vtg8uVw5l3OhCXdzB+wLFxdwkNLNrPzQA0T8lO576ICZo7IxPSABcJFREREpP9QgJOep74altwFK34Dqfnw9ZfghFndXkYgYHllXSkPLN7Etr3VjBmUwlOXFzJ79EAFNxERERGJCAU46Vk+exteuhbKd8DUq2DO7RDXvdPwW2t5feNuHli0iU/KDjEqK4nHL53EvDHZCm4iIiIiElEKcNIz1FbC4jtg5VOQfgJ8418w5NRuLcFay9JP93L/ok2sK6nghMxEHrpkAp8vGIQrSsFNRERERCJPAU4ib8tiePkGOLQLpl8Ls2+F2IRuu7y1lne37ueXr3/Kf3aWk58ez88vKuCLE3OJdkV1Wx0iIiIiIkeiACeR4z0Ir90Ga/4EmaPhm69D/pRuLeGDzw7wy9c/ZcVnB8jxuLn7i+O4aHIesdEKbiIiIiLS8yjASWR8+i/4x/egei987odw2k0Q4+62y68pKueXr3/KO5v3MSA5jjvPP5lLpg7GHePqthpERERERI6VApx0r5oD8K+bYN3zkDUWvvJXGDSx2y6/YVcFDyzaxOKP95CeGMt/n3siXztlKPGxCm4iIiIi0vMpwEn32bAQXv2RM3Ry1i0w8wcQHdstl968+xAPLN7Eq+vKSHFH86OzRnH5jGEkxel/ARERERHpPfTpVbpe1R4nuG18CXImwNcWQvbYbrn0Z/uqeWjxJl76aBeJsdFcf8YIvvW5E/DEx3TL9UVEREREOpMCnHQda2HdC86QyfoqmHMHnHo9uLr+P7uiAzX8aslmXlxdQqwriqtOG85Vp51AWmL3dPxERERERLqCApx0jcpS+Of3YdO/IG8KXPAIDBjd5ZctrfDy6ze28NzKIowxXDZ9KN+dNZwByXFdfm0RERERka6mACedy1pY82f493+Dvx7m3Q3TvgNRXTtJyJ5DtTy2dCt/XrETay0XT8nn2tkjyfZ038yWIiIiIiJdrUMBzhhzNvAQ4AJ+Z629t9X+rwI/Dj6tAr5rrf2oI9eUHqy8CP5xA2xdAkNmwBcehozhXXrJA9X1/Obtrfzh3e34/JYvTcrlujNGkp/efQuBi4iIiIh0l+MOcMYYF/AIcCZQDHxojHnZWrsx5LDPgNOttQeNMecATwDTOlKw9ECBAKz6PSy63enAnfsLKPwWRHXdYtgVXh+/e2cbTy37jBqfn/kTcrl+zkiGZSZ22TVFRERERCKtIx24qcAWa+02AGPMX4ELgKYAZ619N+T494G8DlxPeqIDn8HL18H2d+CEWXD+ryBtSJddrqqugd8v+4zfvrONytoGzhuXw/fmjmRkVnKXXVNEREREpKfoSIDLBYpCnhdz+O7at4B/tbfTGHMlcCXA4MGDO1CWdItAAD74DSy5C6KineA26etgTJdczlvv55n3tvP4W1s5WONj7klZfP/MkYwZ5OmS64mIiIiI9EQdCXDhPqnbsAcaMxsnwM1s72TW2idwhlhSWFgY9jzSQ+zbDC9dC0Xvw8iz4PMPgie3Sy5V6/PzlxU7eXTpVvZV1XHaqAH84MxRTMhP7ZLriYiIiIj0ZB0JcMVAfsjzPGBX64OMMQXA74BzrLX7O3A9iTR/A7z/CLx5N0S74Yu/gYKLu6TrVt8Q4LmVRfz6jS2UVdZyygnpPHbpJKYMTe/0a4mIiIiI9BYdCXAfAiONMcOAEuAS4CuhBxhjBgMvAl+z1m7qwLUk0vZ8DAuvhl3/gRM/D+f9EpKzO/0yDf4AL64u4VdLNlN80MvkIWncv2A8p47I7PRriYiIiIj0Nscd4Ky1DcaYa4HXcJYReMpau8EY853g/seB24EM4FHjdGkarLWFHS9buo3fB8sehLfuA3cKXPQUjLmw07tu/oDln2t38eDizXy2r5pxuR5+On8ss0YNwHTRfXUiIiIiIr2Nsbbn3W5WWFhoV65cGekypHQtvHQ1lK1zQtu5P4fEzu2EBQKW1zaUcf+iTWzeU8WJ2cn84MxRnHlyloKbiIiIiPRbxphV4ZpfHVrIW/qohjp4+xew7H6IT4eL/wQnnd+pl7DWsuTjPdy/aBMbSysZPiCRX39lIueOzSEqSsFNRERERCQcBThpqWQVLLwG9n4M4/8L5t0NCZ03cYi1lnc27+OXizbxUVE5g9MTuH/BeC6YkItLwU1ERERE5LAU4MTh88LSe+DdhyEpG77yPIw6q1Mv8f62/dz/+iY+2H6A3NR47r1wHF+anEeMK6pTryMiIiIi0lcpwAnsXAEvXQP7N8Oky+Csn4K78xbIXrXjIPcv+pTlW/YzMDmOuy4Yw8VT8omLdnXaNURERERE+gMFuP6svhqW/BRWPA6efPjaQhg+u9NOv664gvsXfcqbn+4lIzGW2847iUtPGYI7RsFNREREROR4KMD1V5+9Ay9fCwe3w5Rvw9w7IS6pU079SVklDyzaxGsbduOJj+Gms0dz2fShJMbpPzcRERERkY7QJ+r+pu4QLLoDVj4JacPg8ldh6IxOOfWWPVU8uHgTr6wrJSk2mu/NHck3Zw4jxR3TKecXEREREenvFOD6ky1L4B83QEUxTL8WZt8KsQkdPu3O/TU8uGQTC1eX4I5x8d3Th3PlaSeQmhDbCUWLiIiIiEgjBbj+wFsOr98Kq/8EmaPgW69D/tQOn7ak3Muv39jM8yuLcUUZvjVzGFedPpzMpLhOKFpERERERFpTgOvrPv03/PN7ULUHZn4fTr8ZYtwdOuWeyloeeXMLz35QhMXy1WmDuXr2CLJSOnZeERERERE5PAW4vqrmAPz7Zlj7fzBwDFzyF8id1KFT7q+q4/G3tvLMeztoCFgWFOZx7RkjyU2N76SiRURERETkcBTg+qKNL8MrPwTvAafj9rkfQvTx349WXlPPE29v4+l3t1Pr8zN/Yi43zBnJkIzETixaRERERESORAGuL6naC6/+CDYuhOwC+NqLkD3uuE93qNbHk8s+48l3PuNQXQOfL8jhe3NHMWJg5yw3ICIiIiIix0YBri+wFtb/DV69EeqrYM7tcOr14Dq+6ftr6ht4+t3tPPH/2bvz8Cjre/3j728mK0lIIAtLAgTCpuyKuCFWqVtRsXVfsLa1drNWPVrraevS03Pa/mxt7anVY1uruFStu9a6a5G6AcriigpBErYQSMi+zHx+fzyTZJJMIECSJ5Pcr+uaa+ZZ5zOZIeSe7/IsWUd5TSMnTBnGFcdNZPLwwd1cuIiIiIiI7A0FuFhXuQWevhI+/gfkzYaFt0Lu5H06VV1jkHvf3MBtr35GWXUDx0zK4crjJjEtP6ObixYRERERkX2hABerzGDl/fDctdBUD8f/HA77LsQF9vpU9U1BHly2kVtf+ZStu+o5cnwWVx43iYPHDOmBwkVEREREZF8pwMWiimLvgtyfvgijj4CFf4Cswr0+TWMwxCMrivnflz+lpLyWQwqG8LuzZ3F4YVYPFC0iIiIiIvtLAS6WmMGKu+D5n4KF4KSb4JCLIS5ur04TDBlPrCzhlpc+YUNZDTNGZfKLr0zjqAnZOOd6pnYREREREdlvCnCxYmcRPPl9WL8Exs6DU/8XhhTs1SlCIeOZ9zbz2xfW8llpNQeOGMyfL5zN/ANyFdxERERERGKAAlxfFwrBsj/BizeAC8DJv4ODL4K9CFxmxgsfbOXmF9by0ZZKJuSmcdv5B3HClOHExSm4iYiIiIjECgW4vqzsM3jie/D5GzD+i3DKLZCR3+XDzYxX15by2xfWsrq4goKsQfzu7JmcMmMkAQU3EREREZGYowDXF4WC8OYf4eWfQ3wSnHYbzDh3r1rdXv90O795YS0rNuwkLzOF/3fGdL4yK4/4wN6NlxMRERERkb5DAa6v2faR1+pWshwmfQkW3AyDR3T58GVFO/jN8x/z5rodDB+czM9Pm8pZs0eRGK/gJiIiIiIS6xTg+opgI/z7FvjXryAxDU7/C0w9vcutbqs2lvObF9ayZG0p2WlJXHfygZx36GiSE/b+unAiIiIiItI3KcD1BVvWwOPfhS2r4cDT4Eu/hrScLh36waZd3PzCWl78cCtDBiVw7UmTWXT4GAYl6q0VEREREelv9Fe+n5oa4LVfw2u/gZQhcNZiOHBhlw79ZGslv3vxE/6xZjPpyfH8x3ETuejIAtKTE3q4aBERERER8YsCnF9K3vHGum37AKafDSf+EgYN3eNhRdurueWlT3h8ZQmDEgJ8/9jxXDx3HBmDFNxERERERPo7Bbje1lgHr/4CXv89pA2Dcx+ESSfu8bCNO2r435c/4ZF3SkgIOC45ahzfOrqQoamJvVC0iIiIiIj0BQpwvenzt7xWt7JPYNYiOP7nkJK520O2VNTxh1c+4cFlG3E4Fh02hu8eU0huenIvFS0iIiIiIn2FAlxvaKjxrun25h+9C3Ff8CiMn7/bQ0or67nt1c+4960NhELG2YeM4tJjxzMiI6WXihYRERERkb5GAa6nFS2FJy6Fneth9jfguBshKb3T3XdWN/B/S9Zx9+tF1DcFOf2gfC6bP4FRQwf1YtEiIiIiItIXKcD1lPpKePEGWPZnGFIAX30axh7V6e4VtY385bV13PnvIqobmjh1xkh+MH8C43LSeq1kERERERHp2xTgesJnL8OTP4CKjXDYd+HYn0BiatRdq+qbuOvf67ljyTp21TVx0tThXHHcRCYO67yVTkREREREBiYFuO5UVwHP/wTeWQxZ4+Hrz8Low6LuWtsQ5J43i7j9X+vYUd3AFw/I5fIvTmRqXkYvFy0iIiIiIrFCAa67rH0OnrocqrbAkT+AL1wLCR0nHKlvCvK3tz7n1lc/o7SynqMmZHPlcROZNXqID0WLiIiIiEgsUYDbXzU74NlrYfUDkHMAnHMv5B3cYbfGYIi/Ly/mf1/+hM0VdcwZO5RbzzuIOWP3fPFuERERERERUIDbPx8+BU9fCbU7YN4PYd5VEJ/UZpemYIjH3i3h9y9/wsYdtcwanclNZ8zgyPFZOOd8KlxERERERGKRAty+qN4Oz1wF7z8Gw6fBBY/AiOltdgmFjKdWb+KWFz9h3fZqpuYN5mcXTeULk3IU3EREREREZJ8owHXF6ofgpZ9BRTGkDIWmOgg2eLNLHnk5BBJadjUznnt/Cze/sJa1W6uYNCyd2y84mBOmDFNwExERERGR/bJfAc45dyJwCxAA/mxmv2y3fTLwV+Ag4Mdm9uv9eT5frH4InroMGmu95doycA7mXw9zr2jZzcx4+aNt3PzCWt7ftItxOan8/txZnDxtBHFxCm4iIiIiIrL/9jnAOecCwK3AcUAxsMw596SZfRCx2w7gMuC0/arSTy/9rDW8NTODZX+BuVdgZiz9dDu/eX4tKzeWM2poCr8+cwanzRxJfCDOn5pFRERERKRf2p8WuDnAp2a2DsA59wCwEGgJcGa2DdjmnFuwX1X6qaK40/VvrSvjNy+s5e31OxiZkcwvvjKNMw7OJ0HBTUREREREesD+BLg8YGPEcjFw6L6ezDl3CXAJwOjRo/ejrG6WkQ8VGzusLg3kcPYdb5KTnsSNp07hnDmjSIoP+FCgiIiIiIgMFPvTVBRtYJft68nM7A4zm21ms3NycvajrO61rPD71Fpim3U1lsj/azyLH3/pAJZcfQxfPaJA4U1ERERERHrc/gS4YmBUxHI+sGn/yul7Lv9gAtc0XkxxKJuQOYpD2fyo8WKWphzLN+eNIyVRwU1ERERERHrH/nShXAZMcM6NBUqAc4DzuqWqPmRTeS0lzOXJhrlt1ruKOp8qEhERERGRgWqfA5yZNTnnLgWew7uMwJ1m9r5z7tvh7bc754YDy4HBQMg5dzlwoJnt6obae8XIzBRKymujrhcREREREelN+3UdODN7Bnim3brbIx5vwetaGbOuPmES1z66htrGYMu6lIQAV58wyceqRERERERkINqvADcQnDYrD4CbnvuYTeW1jMxM4eoTJrWsFxERERER6S0KcF1w2qw8BTYREREREfGdrjgtIiIiIiISIxTgREREREREYoQCnIiIiIiISIxQgBMREREREYkRCnAiIiIiIiIxQgFOREREREQkRjgz87uGDpxzpcAGv+uIIhvY7ncR0m/p8yU9SZ8v6Un6fElP0udLelpf/YyNMbOc9iv7ZIDrq5xzy81stt91SP+kz5f0JH2+pCfp8yU9SZ8v6Wmx9hlTF0oREREREZEYoQAnIiIiIiISIxTg9s4dfhcg/Zo+X9KT9PmSnqTPl/Qkfb6kp8XUZ0xj4ERERERERGKEWuBERERERERihAKciIiIiIhIjFCA6wLn3InOuY+dc586537kdz3Svzjn7nTObXPOved3LdL/OOdGOedecc596Jx73zn3A79rkv7DOZfsnHvbObcq/Pm60e+apP9xzgWcc+865572uxbpX5xzRc65Nc65lc655X7X01UaA7cHzrkAsBY4DigGlgHnmtkHvhYm/YZzbh5QBSw2s6l+1yP9i3NuBDDCzN5xzqUDK4DT9DtMuoNzzgGpZlblnEsAlgI/MLM3fS5N+hHn3JXAbGCwmZ3sdz3SfzjnioDZZtYXL+LdKbXA7dkc4FMzW2dmDcADwEKfa5J+xMyWADv8rkP6JzPbbGbvhB9XAh8Cef5WJf2FearCiwnhm74Zlm7jnMsHFgB/9rsWkb5CAW7P8oCNEcvF6I8fEYlBzrkCYBbwlr+VSH8S7t62EtgGvGBm+nxJd/od8EMg5Hch0i8Z8LxzboVz7hK/i+kqBbg9c1HW6dtFEYkpzrk04BHgcjPb5Xc90n+YWdDMZgL5wBznnLqCS7dwzp0MbDOzFX7XIv3WkWZ2EHAS8L3wsJY+TwFuz4qBURHL+cAmn2oREdlr4bFJjwD3mdmjftcj/ZOZlQOvAif6XIr0H0cCp4bHKT0AHOucu9ffkqQ/MbNN4fttwGN4Q6f6PAW4PVsGTHDOjXXOJQLnAE/6XJOISJeEJ5n4C/Chmd3sdz3SvzjncpxzmeHHKcAXgY/8rUr6CzO71szyzawA7++v49uHcAAAIABJREFUl83sAp/Lkn7COZcantwL51wqcDwQEzOCK8DtgZk1AZcCz+EN/n/IzN73tyrpT5xzfwPeACY554qdc9/wuybpV44EFuF9c70yfPuS30VJvzECeMU5txrvC88XzExTvYtILBgGLHXOrQLeBv5hZs/6XFOX6DICIiIiIiIiMUItcCIiIiIiIjFCAU5ERERERCRGKMCJiIiIiIjECAU4ERERERGRGKEAJyIiIiIiEiMU4EREpN9yzgUjLp+w0jn3o248d4FzLiauGSQiIv1HvN8FiIiI9KBaM5vpdxEiIiLdRS1wIiIy4Djnipxzv3LOvR2+jQ+vH+Oce8k5tzp8Pzq8fphz7jHn3Krw7YjwqQLOuT855953zj3vnEvx7UWJiMiAoAAnIiL9WUq7LpRnR2zbZWZzgD8Avwuv+wOw2MymA/cBvw+v/z3wLzObARwEvB9ePwG41cymAOXA6T38ekREZIBzZuZ3DSIiIj3COVdlZmlR1hcBx5rZOudcArDFzLKcc9uBEWbWGF6/2cyynXOlQL6Z1UecowB4wcwmhJevARLM7Oc9/8pERGSgUguciIgMVNbJ4872iaY+4nEQjS0XEZEepgAnIiID1dkR92+EH78OnBN+fD6wNPz4JeA7AM65gHNucG8VKSIiEknfFIqISH+W4pxbGbH8rJk1X0ogyTn3Ft6XmeeG110G3OmcuxooBb4WXv8D4A7n3DfwWtq+A2zu8epFRETa0Rg4EREZcMJj4Gab2Xa/axEREdkb6kIpIiIiIiISI9QCJyIiIiIiEiPUAiciIr3COWcRF8y+3Tn3067suw/Pc75z7vl9rVNERKQvUwuciIh0iXPuOeAtM7uu3fqFwP/hXSetaTfHGzDBzD7twnN1ad/wtdjW411/rdPnFhER6S/UAiciIl11F7DIOefarV8E3KcA1bOcc5o5WkREFOBERKTLHgeGAkc1r3DODQFOBhY75+Y4595wzpU75zY75/7gnEuMdiLn3F3OuZ9HLF8dPmaTc+7r7fZd4Jx71zm3yzm30Tl3Q8TmJeH7cudclXPucOfcRc65pRHHH+GcW+acqwjfHxGx7VXn3H855/7tnKt0zj3vnMvupOYhzrmnnXOlzrmd4cf5EduHOuf+Gn4NO51zj0dsW+icWxl+DZ85504Mry9yzn0xYr8bnHP3hh8XhLuSfsM59znwcnj9351zW8KvZ4lzbkrE8SnOud845zaEty8Nr/uHc+777V7PaufcadFeq4iI9F0KcCIi0iVmVgs8BFwYsfos4CMzW4V3fbQrgGzgcGA+8N09nTccZq4CjgMmAF9st0t1+DkzgQXAdyKCx7zwfaaZpZnZG5EHOueGAv8Afg9kATcD/3DOZUXsdh7e9d5ygcRwLdHEAX8FxgCjgVrgDxHb7wEGAVPC5/ptuIY5wGLg6vBrmAcUdfbziOJo4ADghPDyP/F+TrnAO8B9Efv+GjgYOAIvbP8QCAF3Axc07+ScmwHkAc/sRR0iItIHKMCJiMjeuBs40zmXEl6+MLwOM1thZm+aWZOZFeGNizu6C+c8C/irmb1nZtXADZEbzexVM1tjZiEzWw38rYvnBS/wfWJm94Tr+hvwEXBKxD5/NbO1EQF1ZrQTmVmZmT1iZjVmVgn8d3MdzrkRwEnAt81sp5k1mtm/wod+A7jTzF4Iv4YSM/uoi/UD3GBm1eH6MLM7zazSzOrxflYznHMZzrk44OvAD8LPETSz18P7PQFMcM5NCJ9zEfCgmTXsRR0iItIHKMCJiEiXmdlSoBRY6JwbBxwC3A/gnJsY7la4xTm3C/gfvNa4PRkJbIxY3hC50Tl3qHPulXDXxQrg2108b/O5N7RbtwGv9anZlojHNUBatBM55wY55/4v3D1xF173zUznXAAYBewws51RDh0FfNbFeqNp+dk45wLOuV+Gu2HuorUlLzt8S472XOEQ9xBwQTjonYvXYigiIjFGAU5ERPbWYryWt0XA82a2Nbz+NrzWrQlmNhj4T6D9hCfRbMYLOc1Gt9t+P/AkMMrMMoDbI867p6mUN+F1eYw0GijpQl3t/QcwCTg0/Pqau286vJA11DmXGeW4jUBhJ+esxut22Wx4lH0iX+N5wEK8bqYZQEFEDduBut08193A+XhdW2vadzcVEZHYoAAnIiJ7azFegPgm4e6TYenALqDKOTcZ+E4Xz/cQcJFz7kDn3CDg+nbb0/Fat+rC48nOi9hWijfGa1wn534GmOicO885F++cOxs4EHi6i7W1r6MWb8KUoZF1mtlmvLFpfwxPdpLgnGsOeH8Bvuacm++ci3PO5YV/PgArgXPC+88GzuhCDfVAGV7w+5+IGkLAncDNzrmR4da6w51zSeHtb+D9rH6DWt9ERGKWApyIiOyV8Pi214FUvJaxZlfhhatK4E/Ag1083z+B3+HNsvhp+D7Sd4GfOecqgevwAl/zsTV4Y9H+HZ798rB25y7DmyXzP/BCzw+Bk81se1dqa+d3QApeS9ebwLPtti8CGvFaIbcBl4dreBtvkpTfAhXAv2htFfwpXovZTuBGwt1Rd2MxXhfQEuCDcB2RrgLWAMuAHcCvaPt//WJgGnDvHp5HRET6KF3IW0REZIBwzl0IXGJmc/2uRURE9o1a4ERERAaAcPfU7wJ3+F2LiIjsOwU4ERGRfs45dwLeeMGt7LmbpoiI9GHqQikiIiIiIhIj1AInIiIiIiISI+L9LiCa7OxsKygo8LsMERERERERX6xYsWK7meW0X98nA1xBQQHLly/3uwwRERERERFfOOc2RFuvLpQiIiIiIiIxQgFOREREREQkRijAiYiIiIiIxIg+OQZORKQ3NTY2UlxcTF1dnd+liEgflJycTH5+PgkJCX6XIiKiACciUlxcTHp6OgUFBTjn/C5HRPoQM6OsrIzi4mLGjh3rdzkiIupCKSJSV1dHVlaWwpuIdOCcIysrSy30ItJnqAVORAQU3kT2Rc0OqNwMwQYIJEL6CBg01O+qup1+P4hIX6IAJyIiInuvZgdUbAQLecvBBm8Z+mWIExHpK7oU4JxzJwK3AAHgz2b2y3bbFwL/BYSAJuByM1sa3lYEVAJBoMnMZndb9SIiPnj83RJueu5jNpXXMjIzhatPmMRps/L8Lks6s/oheOlnUFEMGfkw/zqYfpbfVcUWMwg1eSGt+Va5pTW8tewX8kJcqAkCCRCX0Hofp1EbIiLdYY8BzjkXAG4FjgOKgWXOuSfN7IOI3V4CnjQzc85NBx4CJkdsP8bMtndj3SIivnj83RKufXQNtY1BAErKa7n20TUAvRbi0tLSqKqq6pXn6i4FBQUsX76c7Ozs3n3i1Q/BU5dBY623XLHRW4ZeC3Ex8X6FQm3CWcGBs1j+0pNkZ6aF1zUC1rVzWQh2lXRc7wJtQ12bgBffus7tW9BbuXIlmzZt4ktf+tI+HS8iA1CMfsHXlRa4OcCnZrYOwDn3ALAQaAlwZhb5P1MqXf4tLyLSt9z41Pt8sGlXp9vf/bychmDbVofaxiA/fHg1f3v786jHHDhyMNefMqVb65Swf/4ItqzpfHvxMgjWt13XWAtPXAor7o5+zPBpcNIvo2+LRW1azxrbtqI1rws1tT0mFIT6KrBUSEiFlARvjFsgMRy0EqH0Y+/49gKJkD0JQo3hc7e7DzZCQ33nobA56HUa9hIgEN8h6K1cuZLly5crwIlI1/SBL/j2VVe+5soDNkYsF4fXteGc+7Jz7iPgH8DXIzYZ8LxzboVz7pLOnsQ5d4lzbrlzbnlpaWnXqhcR6WXtw9ue1nfFNddcwx//+MeW5RtuuIEbb7yR+fPnc9BBBzFt2jSeeOKJLp2rqqqq0+MWL17M9OnTmTFjBosWLQJg69atfPnLX2bGjBnMmDGD119/Pep5q6urWbBgATNmzGDq1Kk8+OCDADzzzDNMnjyZuXPnctlll3HyyScDUFZWxvHHH8+sWbP41re+hZlP3+u1D297Wt8Ffe79CoWgqQ7qK6GmDHZtprr4QxYc9wVmTJnE1AMm8OAdv4Hta3nm4XuYPPNQ5h53Cpf98DpOPv87kJxBWUMSxy+6kllfuohvXX8rFpcAww6AnIkwtAAG50FqDiRnUFSyjckHTuXia37J1GPP5PxLf8yLS97iyIVfY8Lchbz98SYIxLOjspbTzrmQ6Ycdw2HHncbqDTth6DhuuPVvfPWH/4/jL/ohBUd8mUeXfsQPf30X0447nxMvvJLG+DQIJLHi3VUc/aXTOXjufE5YcCqbP3obtq/lC/OO4JpLv86cWdOYWFjAa08/QMO2T7nupz/mwQceYOb0aTx4393ccN1P+fVNN7X8PKdOnUpRURFFRUVMnjyZiy++mKlTp3L++efz4osvcuSRRzJhwgTefvvtff5siEgfZgZ1FVD2GWxcBs9e2xremjXWei1yfZ2Z7fYGnIk37q15eRHwv7vZfx7wYsTyyPB9LrAKmLen5zz44INNRKS3fPDBB13e94hfvGRjrnm6w+2IX7y0z8//zjvv2Lx581qWDzjgANuwYYNVVFSYmVlpaakVFhZaKBQyM7PU1NROz9XY2Bj1uPfee88mTpxopaWlZmZWVlZmZmZnnXWW/fa3vzUzs6amJisvL4963ocfftguvvjiluXy8nKrra21/Px8W7dunZmZnXPOObZgwQIzM/v+979vN954o5mZPf300wa0PHevunmK2fWDO95unrLPp+zV92vbNrOmBivbvNGsZqed9ZVT7bf/c71Z2WfWtPl9K//432Yl73S4Pfynm+3iC840K1tnVl5s5cWfWu2OLZafn2frPllrFgrt8/u1fv16CwQCtnr1agtWltpB0w+0r519qoU2r7HHH7zHFi5caGZml156qd1www1mZvbSSy/ZjBkzzMzs+uuvtyOPPNIaGhps5cqVlpKSYs8884yZmZ122mn22GOPWUNDgx1++OG2bds2s1DIHrj/XvvaVxeZ1Zbb0XOPsCsv/ZbZzg32j7/92eYfdZjZ5jX215tvsO9ddFbLz+D6Ky+xm356udnmNWbbPrQpkyfY+lVLbf2at7z6l/3bgrWVdtBBs+xrF11koVDIHn/88Zb629ub3xMi0gvqq812fm5W8q7ZJy+arXrI7I3bzF76udlTl5s9uMjsrwvMbj3M7KYJZjcOjf7/QYdbht+vrAWw3KJkpa50oSwGRkUs5wObdhMIlzjnCp1z2Wa23cw2hddvc849htclc0nXI6aISN9x9QmT2oyBA0hJCHD1CZP2+ZyzZs1i27ZtbNq0idLSUoYMGcKIESO44oorWLJkCXFxcZSUlLB161aGDx++23OZGf/5n//Z4biXX36ZM844o2UM2tCh3iyBL7/8MosXLwYgEAiQkZER9bzTpk3jqquu4pprruHkk0/mqKOOYuXKlYwbN67l4sbnnnsud9xxBwBLlizh0UcfBWDBggUMGTJkn38++2X+dW27yAAkpHjr91G3vl+hIP/5ox+y5LWlxDnnHbd2BS8/+SRnnPQFshtLYGsxQwF2lvLyq0tY/JufQFMdgYREMnLz23ZrDD+eNi+Vq/7791zzq9vbvV+FjB0/Adi/92vs2LFMmzYNgCkzDmb+CSfghk9l2uxBFP3PrwFYunQpjzzyCADHHnssZWVlVFRUAHDSSSeRkJDAtGnTCAaDnHjiiYD3OSsqKuLjjz/mvffe47jjjgMgGAwyYsQISM6AQAJfOWcRZI7m4GNOpugnv4DhUyFzGQzaAtkTIdgESYMhMQWSB3vdNc28VspQkLGjRjJtZArs+IQpY0cy/+AJuK3vM21kKkWfrYXyjR27cIaC3jl0SQGR7hds9Ga2rSmLuG2Psq7MW1e9HZpqOzmZ82bCHZTl3YaOg/xDWpebb09+D6q2dTw8I79HX2p36EqAWwZMcM6NBUqAc4DzIndwzo0HPjMzc84dBCQCZc65VCDOzCrDj48HYqBdUkQkuuaJSrp7FsozzjiDhx9+mC1btnDOOedw3333UVpayooVK0hISKCgoKBLFxLu7Dgz269rWU2cOJEVK1bwzDPPcO2113L88cdzyimn7PaYPnHtrOZxDN08SL1L71dtrRckAGrLo45Bu+9vj1JavI4VT9/pHXfoAup2bccsiAskeN0WI8NZXLw3Ri85ebf19fT7lZSU1PI4Li6uZTkuLo6mJu81W5Rus83PEbl/QkJCy/rm482MKVOm8MYbb+z2+QOBQMvz4Zw3fi4xFYD4QRmEEhMhczQAdU0GOZPBQiSlpoeDXiNxyWkkDc6GpHTi4rfT1NgItTvBgm2fdNc2+PkxkDYc0odB+nDvunfR7pMzFfRk4AqFoK48HL62Rw9gNWVeCGterq/o/HxJg1tDV9pwyJ3SNqClZrcNZskZEBfYc53H/3e3f8HXW/YY4MysyTl3KfAc3mUE7jSz951z3w5vvx04HbjQOdcI1AJnh8PcMOCx8C/meOB+M3u2h16LiEivOG1WXrfPOHnOOefwzW9+k+3bt/Ovf/2Lhx56iNzcXBISEnjllVfYsGFDl85TUVER9bj58+fz5S9/mSuuuIKsrCx27NjB0KFDmT9/PrfddhuXX345wWCQ6upqBg8e3OG8mzZtYujQoVxwwQWkpaVx1113cfXVV7Nu3TqKioooKChoGRcHMG/ePO677z5+8pOf8M9//pOdO3d2zw9qX0w/q9sHpLe+X6X86/lneOjvfyc3M42E6s288uoS7+de+iEklXuzMu5cHz4yrjWQJWdQ0RBHbt4YEoYfwCuvvc6G4s2QM5n5C0d579d/3khWRvP7Nch7v26/PSber+Zz/vSnP+XVV18lOzs7aq3RTJo0idLSUt544w0OP/xwGhsbWbt2LVOmdD4ZUHp6OpWVlS3LBQUFPP300wC88847rF8ffg9cHOBagh7xyd4fg0PGQIVBfBKMmO79ERo5Acu2Rjj8e97lEyo3w/ZPYP0Sb0xNe/HJkDas84DXfJ+UrqAnfZsZNFR1DF+Rt+p2LWW1OzpeYqRZfDIMym4NYEMKIkLY0I6tZClDIT6xZ15bD33B1xu6dB04M3sGeKbdutsjHv8K+FWU49YBM/azRhGRfm/KlClUVlaSl5fHiBEjOP/88znllFOYPXs2M2fOZPLkyXs+CXR63JQpU/jxj3/M0UcfTSAQYNasWdx1113ccsstXHLJJfzlL38hEAhw2223cfjhh3c475o1a7j66qtbWkxuu+02UlJS+OMf/8iJJ55IdnY2c+bMadn/+uuv59xzz+Wggw7i6KOPZvTo0d3zg+otZl6XuciZGiNazqZkGZU7S8nLzmREYhXnn3QYp3z1AWYffRIzpx3I5AmFkDzUm/zDxXmzMjZPlx/xB/v53/iO934deUy/e79uuOEGvva1rzF9+nQGDRrE3Xd3MutnFImJiTz88MNcdtllVFRU0NTUxOWXX77bAHfMMcfwy1/+kpkzZ3Lttddy+umns3jxYmbOnMkhhxzCxIkT9+4FxMVBXJIX6MALW1+8oeN+DTVQtSUc7La0Brzm+63vw6cvQUNlx2MTBu0+4KWP8IJgUtre1S7Smab6zrskRmslqynrfNInF2gbtnIndwxgbUJZNiQO6t3Xuyc98AVfb3DRujj4bfbs2bZ8+XK/yxCRAeLDDz/kgAMO8LuMmFRVVUVaWhpmxve+9z0mTJjAFVdc4XdZe2ahiFDWfmr98HKHb5Bda1fG+MgxZ+ExaHGJff5i1TH7fvUB+/17or4Kqra2DXfR7htrOh6bmB4OdJ2FvXBrX0LKvtcnsScU9Lr7tmkJ66ylLNxK1rCba1ImZ7brkhilRSyy9Sw5Qy3IPcw5t8LMZrdf36UWOBERkWj+9Kc/cffdd9PQ0NByyQDfmXnjl5rDWFOUcBZq7HhcXHw4nCV5rS3tr3vWrvUsFvXJ92ugSErzblmFne9jBvW7oHI3QW/jW959tFaR5Iw9t+alD29tVZS+o/m9b98dsUOrWERIqy2n00svJ6a1DWDZEyJCWXaUropDvOsrSkxQC5yIDHix2AK3Zs2almuDNUtKSuKtt97ar/OWlZUxf/78DutfeuklsrKy9uvc3aal9ayTlrPdtp5FaTlrmSCk51rPYvH9ionPQi/qU78nzLxJInbXktccAqN9WZEyNErAa9fClzbM+/ch+6ahpvMuiTXbo7eShZqinysuIUqrWHYn3RTDy2qN7Rc6a4FTgBORAe/DDz9k8uTJfWPWRPH+iGkfzpoiljttPdtdOIv91jPxj5nx0Ucf9Z0A11WhkNfFLlrIa9Odc0vHWTdxXmiI2ooXsZya0/9bbvZ2ivuasuhdYYG2U9x3MnFHy+yK4fvENP3+GqDUhVJEpBPJycmUlZWRlZWlENfTzHbfchZsjP6HZHMYa+namNBu/FkXpowW2QdmRllZGcl7uHRDnxQX54WA1CzvWnmdCQW90NEh6EVMyrJ5NVRv69i67eIgNXf3rXnpI7ywsret3Ksf6v4ZArs6xX3k7Iq7neI+ozWEdecU9yK7oQAnIgNefn4+xcXFlJaW+l1K7LOQ98dgqMm7WTBiOfy4/ZgNF/D+oImLD9+HH7vm+7jwt89N4Vtn32yL9Izk5GTy8/v+xX33WVwA0nK924jdTB4ebILq0taAV9Vu1s1dxVCy3Nunw3PEt47Bi9aS1xL0hnr/3lc/1PYaXRUbvWVoDXFm0FDdeZfE/Z3iPnOMf1Pci+yGulCKiEjXhILeH2oVxeHbRu++fGPruvbfVMfFe1PpZ4yCzFHet+gtt/By8/W4RKR/CDaGu2i2v6xCu26ctTs6HhtI9FqyqrZ4rfIdtid5F2HfmynuOwtg7ceT9bUp7mXAUxdKEZGBaG+6INVXtg1nkcGsohh2lXTs3pic6QWzIWOg4MiIcDbau0/LVXchkYEmkND6u2B3murDLXlRZt1c9bfoxwTrvd85I2dEmd4+IpRpinvpxxTgRET6q2hdkJ641JuGPGNU23BW8TnURWs9G+ntO+bwiFaz5pa0PG9MmojIvohP8r78GTKm47aipd7vrPYyRsG5nYQ7kQFCAU5EpD+q2QHP/qg1vDUL1sOyP3uPkzNaw9jowzp2bUwfrtYzEfHH/OvafgEF3tT486/zryaRPkIBTkSkP6gthw2vQ9FrsP412PoenV7gFQc/+hySB/dmhSIiXdfc1bu7Z6EU6QcU4EREYlFdBXz+Jqxf4oW2zasB82ZRGzUHjvkxvH2HN+13exn5Cm8i0vdNP0uBTSQKBTgRkVhQXwmfvwVFS7wWts0rvamwA4mQPwe+8CMoOAryZ3vjSsAbV6IuSCIiIv2KApyISF/UUO21sBUt9VrYSt7xZoCMS/BC2lFXwdijIP8QL5RFoy5IIiIi/Y4CnIhIX9BY680Ouf41L7SVrIBQozcTZN7BMPdyr4Vt1KF7d60idUESERHpVxTgRET80FgHxcu81rWipd7jYIN38dmRs+Dw73ktbKMOg6Q0v6sVERGRPkIBTkSkNzTVe61q61/zQtvGt70p/V0cjJgBh34LCuZ50/lrghERERHphAKciEhPCDZ649aaJx3Z+DY01QIOhk+DQy72WthGHw4pmX5XKyIiIjFCAU5EpDsEm7yZIZun9f/8LWis9rYNmwoHXwQFc2HMETBoqK+lioiISOxSgBMR2RehIGxe1Xrh7M/fgIYqb1vOATDr/HBgmwupWf7WKiIiIv2GApyISFeEgrD1vdYxbBteh/pd3rbsiTD9bC+wFRwFaTn+1ioiIiL9lgKciEg0oRBse9+bIXL9a7Dh31BX7m0bWghTv+KFtYK5kD7c31pFRERkwFCAExEBMINtH4an9X8Niv4NtTu8bUMK4IBTYOw8L7ANHulrqSIiIjJwKcCJyMBkBtvXhicdWerdarZ72zJGw6STWlvYMkf5W6uIiIhImAKciAwMZlD2mTetf3Ngq9rqbRucB+O/6E3rX3AUDBnjb60iIiIinVCAE5H+yQx2rm+ddKRoKVRu9ralDQ93hzzKC21DxoJz/tYrIiIi0gUKcCLSf+zc0Dqtf9FS2FXsrU/N9bpCjj0KCuZBVqECm4iIiMQkBTgRiV0VxREtbK9B+efe+kFZ4Sn9L/da2rInKrCJiIhIv6AAJyKxY9em8LT+S7zAtrPIW58yxAtsh1/qdYvMmQxxcb6WKiIiItITFOBEpO+q3Nraurb+Ndjxmbc+OQPGzIU53/K6ReZOUWATERGRAUEBTkT6jqrS1glHil7zpvkHSBoMY46A2V/zWtiGT4O4gL+1ioiIiPhAAU5E/FNdBhvCU/qvfw1KP/TWJ6bB6MNh5vleC9vwGRDQrysRERER/UUkIr2ndicU/bu1hW3re976hEEw+jCYfpbXwjZyJgQS/K1VREREpA9SgBORnlNXARteb50pcssawCA+GUYdCsf+xJvWf+QsiE/0u1oRERGRPq9LAc45dyJwCxAA/mxmv2y3fSHwX0AIaAIuN7OlXTlWRPqR+krY8AYULfFC25bVYCEIJMGoOfCFa70ukXkHQ3yS39WKiIiIxJw9BjjnXAC4FTgOKAaWOeeeNLMPInZ7CXjSzMw5Nx14CJjcxWNFJFbVV8HGN1svnL3pXbAgxCVA/iEw72qvS2T+IZCQ7He1IiIiIjGvKy1wc4BPzWwdgHPuAWAh0BLCzKwqYv9UwLp6rIjEkIYa2PhW67T+m96BUBPExXutanOv8FrY8udA4iC/qxURERHpd7oS4PKAjRHLxcCh7Xdyzn0Z+AWQCyzYm2PDx18CXAIwevToLpQlIj2usRaKl7WOYSteDqFGcAHIOwiO+L7Xwjb6MEhM9btaERERkX6vKwHORVlnHVaYPQY85pybhzce7otdPTZ8/B3AHQCzZ8+Ouo+I9LCmei+wNU/rX7wMgvXg4mDEDDjsOzB2nhfYktL9rlbZGmAbAAAgAElEQVRERERkwOlKgCsGRkUs5wObOtvZzJY45wqdc9l7e6yI9LKmBihZEZ7WfwlsfBua6gAHI6bDnG96LWxjDofkDL+rFRERERnwuhLglgETnHNjgRLgHOC8yB2cc+OBz8KTmBwEJAJlQPmejhWRXhRs9CYaWb/EC20b34LGGm/bsGlw8Ne8MWxjjoCUIf7WKiIiIiId7DHAmVmTc+5S4Dm8SwHcaWbvO+e+Hd5+O3A6cKFzrhGoBc42MwOiHttDr0VE2gs2weZVXuta0VL4/E1oCM85lHsgzFoEBXO926Ch/tYqIiIiInvkvJzVt8yePduWL1/udxkisScU9K691jzpyIY3oKHS25Y9yWtdK5gLY+ZCWo6/tYqIiIhIp5xzK8xsdvv1XbqQt4j0UaEQbH2vdVr/Da9DfYW3LWs8TDsj3MJ2FKQP87dWEREREdlvCnAisSQUgtIPW1vYipZCXbm3bchYmLIQCuZBwZEweKS/tYqIiIhIt1OAE/Hb6ofgpZ9BRTFk5MP862D6Wd42Myj9ONzCtgQ2/BtqyrxtmaNh8smt3SIz8v17DSIiIiLSKxTgRPy0+iF46jLvgtkAFRvhye/Dule92SGLlkJ1qbdtcD5MON7rDlkwF4aM8a1sEREREfGHApyIn176WWt4a9ZUByvvg/QRMO6YcAvbUTCkAJzzpUwRERER6RsU4ET8VFHcyQYHV36owCYiIiIibcT5XYDIgJacEX19Rr7Cm4iIiIh0oAAn4pc3b/dmkHSBtusTUryJTERERERE2lEXShE/vP0nePYabxbJySfDK/8dfRZKEREREekRj79bwk3Pfcym8lpGZqZw9QmTOG1Wnt9l7ZECnEhvW/ZneOYqmLQAzvgrxCfCzHP9rkpERERkwHj83RKufXQNtY1BAErKa7n20TUAfT7EKcCJ9Kblf4V//AdMPBHOvMsLbyIiIiKyX8yMxqBR1xSkvjFEXWOQ+qYgdY2hqPc3PvlBS3hrVtsY5KbnPlaAE5GwFXfD05d713I7a7HCm4iIiPRLwZCFA5QXpNo+bhuk6htDbUJXXcQ6b59Qm+Prd3OukO1/7ZvKa/e8k88U4ER6w7v3wlM/gPFfhLPugfgkvysSERGRfs7MOgagKOEn2vbdBaW6yNAVvo88V9N+JKk4B8kJAe8WH0dSQoCk8H1yfByZgxJJio8L7xNHUnzb++TI/cOPI++b9z33jjfYsqu+w/OPzEzZnx95r1CAE+lpK++HJy6FwmPg7PsgIdnvikRERPq8WJ1gIpro3ftau/m1bXEK3zcGqeskdHU8R7vWq/CxDU2h/ap7dwEpLSmerNQASQlxJMe33kfbt839HrYnBHpnkvwfnXRAmzFwACkJAa4+YVKvPP/+UIAT6UmrHoTHvwvjjoZz7ld4ExER6YKenGCiffe+3XXza9+9r0NQitK6Vd/Jufane19iIC6iVSmuQ+gZnJLQoYWpTVBqE7Dahqn2oaulxSs+DtePr0nb/DmKxS8JFOBEesrqv8Pj34axR8E5f/Ou7yYiIiJ7dNNzH0edYOL6J9+jpLx2t0Fpd9376puCNAa7v3tfcyjKTEkgeXBSp61WXmiK0q0vIpxFhq3mEBaI679Byk+nzcqLicDWngKcSE947xF47BIYcySc+wAkDvK7IhERkT7HzNi6q55Pt1XxWal3+3RbFSWdTCRRUdvETc99DLDbrnrtu/ftritgp61WbbZ7j3ure5/I7ijAiXS39x+DR74Jow6D8x6ExFS/KxIREfFVQ1OIDWXV4ZBW3RrYtlVR3dDa0paeFE9hbhopCYEOLXAAIzKSeeWqL/T77n0iu6MAJ9KdPngCHv4G5B8C5/9d4U1ERAaUitrGlmD2aWkVn22rZl1pFRt21BCMGAQ2MiOZwtw0zpw9isLcNApzUhmfk0ZOehLOuQ5j4MCbYOKaEyeTnBDw46WJ9BkKcCLd5cOn4OGvQ/5suOBhSErzuyIREZFuFwoZm3fVea1obbo+VrO9qnVa9sRAHAXZg5g0PJ0F00dQmJNGYU4a43JSSU3a/Z+gsTzBhEhPU4AT6Q4fPQN/vwhGzoLzH4akdL8rEhER2S91jUGKyqr5bFt1m/Fp60qr27SMDU6OZ3xuGsdOzmkJaeNz08gfkkL8fowZi9UJJkR6mgKcyP76+Fl46EIYMQMueASSB/tdkYiISJftrG5oCWiR49M27qhpM/V9/pAUCnPSOHRsFoW5XpfHwtw0slITNR5NpBcpwInsj7XPw0OLYPhUuOBRSM7wuyIREZEOQiGjpLw2PC6teQIRr2WtrLqhZb/E+DjGZacyNS+DhTPzGB8enzYuO42URI09E+kLFOBE9tUnL8KDF0DuAbDoMUjJ9LsiEREZ4Ooag6wrrW4b1Eq9iUTqm0It+w1NTaQwJ5Xjpwxr6fZYmJNG3pAUXXNMpI9TgBPZF5++BA+cBzkTYdHjkDLE74pERGSAMDPKqhvCAa26ZWzaZ6Xe9dMs3O3RORg1ZBDjc9OYOz7LC2m5XlAbmpro74sQkX2mACeytz57xQtv2RPgwidh0FC/KxIRkX4oGDI27qhpM4FIc2Arr2ls2S85IY7CnDQOGj2EMw8e5XV7zE2lICtVU+6L9EMKcCJ7Y92/4G/nwtBChTcREekWNQ1NrGvXkvbZtmrWb6+mIdja7TE7LYnCnFS+NG1EywQihTmpjMxIIU7dHkUGDAU4ka4qWgr3nw1DCuCrT0Jqlt8ViYhIjDAzSqvqW1vRWoJaFZsq6lr2i3MwJiuVwpxUvhA5LX9OGhmDEnx8BSLSVyjAiXTFhtfhvjMhc3Q4vGX7XZGIiPRBjcEQn++oaRmf9mnEha4r65pa9ktNDFCYm8ah47IozEltuXba6KxBJMWr26OIdE4BTmRPPn8T7j0DMvLhq09BWq7fFYmIiM8q6xo7dnssrWZDWTWNwdaLpw0bnERhThqnzcyjMCeV8bnpFOamMnxwsq6dJiL7RAFOZHc+fwvuPR0Gj/DCW/owvysSEZFeYmZs3VXfLqR549O27Grt9hgf5xiTNYjCnDSOP3BYy2yP43JSGZysbo8i0r0U4EQ6s3GZF97ScsPhbbjfFYmISA9oaAqxoay6pRXts21VLddRq24ItuyXnhTPuNw0jhyfTWFuasv4tDFZg0gIxPn4CkRkIFGAE4mmeAXc+xVvrNtXn4bBI/2uSERE9lNFbWPLxCHN49PWlVaxYUcNwVBrt8cRGcmMz03jzNmjvPFpud4kIjnpSer2KCK+U4ATaa/kHbjny94lAi56GjLy/K5IRES6KBQyNu+q81rRIrs9llZTWlnfsl9CwDE2O5VJw9O9afnDF7gem5NKWpL+PBKRvqtLv6GccycCtwAB4M9m9st2288HrgkvVgHfMbNV4W1FQCUQBJrMbHb3lC7SAzathHtOg5QMr+UtI9/vikREJIq6xiAbymrahDSvRa2a2sbWbo+Dk+MZn5vGFybmtIS0wtw0Rg1JIV7dHkUkBu0xwDnnAsCtwHFAMbDMOfekmX0Qsdt64Ggz2+mcOwm4Azg0YvsxZra9G+sW6X6bV8PihZA02AtvmaP8rkhEZMArr2loM8tj8+ONO2qI6PVIXmYK43PTOHRsVsv4tPG5aWSlJqrbo4j0K11pgZsDfGpm6wCccw8AC4GWAGdmr0fs/yagZguJLVve88JbYprXbXLIGL8rEhEZMEIho6S8tmXikOaZHj8rraKsuqFlv8T4OMZlpzI1L4OFLdPypzEuO42URF07TUQGhq4EuDxgY8RyMW1b19r7BvDPiGUDnnfOGfB/ZnZHtIOcc5cAlwCMHj26C2WJdJOt78PiUyEhBS56CoYU+F2RiEhMePzdEm567mM2ldcyMjOFq0+YxGmzOh83XNcYjHrttHWlVdQ3hVr2G5qaSGFOKseFp+Rv7vqYNySFQJxa00RkYOtKgIv2m9KirMM5dwxegJsbsfpIM9vknMsFXnDOfWRmSzqc0At2dwDMnj076vlFut22D+HuUyGQ6F0qYOg4vysSEYkJj79bwrWPrmkZb1ZSXsu1j67BzJg3MScc0KrbjE8rKa/Fwv/DOwejhgyiMCeVueOzWsamFeakMTQ10cdXJiLSt3UlwBUDkYOB8oFN7Xdyzk0H/gycZGZlzevNbFP4fptz7jG8LpkdApxIr9v2Edx9CsTFe2Pesgr9rkhEpM8JhYzaxiDVDU3UNgSprg9S29jEfz39QZvJQgBqG4Nc+fdVLSENIDkhjnHZaRw0eghnHjyKwlyv22NBVirJCer2KCKyt7oS4JYBE5xzY4ES4BzgvMgdnHOjgUeBRWa2NmJ9KhBnZpXhx8cDP+uu4kX2WelaL7y5OK/lLXu83xWJiOyXpmCImsYgNfVBahqaqGkIUtMQGbyavCBWH6S2oYnq8PbWfcP39UFqGpvC5wl2CGl7YgY/PfnAcLfHVEZmpBCnbo8iIt1mjwHOzJqcc5cCz+FdRuBOM3vfOfft8PbbgeuALOCP4Zmemi8XMAx4LLwuHrjfzJ7tkVci0lXbP/XCG3jhLWeiv/WIyIDS0BSiJhygahuaqK7vJEiFH3uBKyKItdy3rq9pCNIQMYZsT5yDQQkBBiXFMygxwKBE7z4tKZ7c9CRSE+NJSQyQmhRPSkKA1KQAKYnxpCYGWva/8qGVbK9q6HDuvMwUvjF3bHf+yEREJIIz63vDzWbPnm3Lly/3uwzpj8o+g7sWQLARLvoH5E72uyIR6YPMjPqmENX1bcNU+8fV9ZFhqqlDqGrTqhV+3BTq+v+7gTgXDkyB1lDVEq4CpCTEh8OVtz4yjLU8Tup4fHJC3H5Prd9+DBxASkKAX3xl2m4nMhERka5xzq2Idg3tLl3IW6Rf2LEO7joZgg3emDeFN5GY19n4rD21ajV3D2zbmtUawGoamtiLnEViIM4LSm1atQLkpie3DVOdtGq1CWBJ8eHzBEgM7H/Q6inNIW1vZqEUEZH9pwAnA8OO9XDXKdBU53WbHHag3xWJ9Iq9nea9pzSPz2oei7W7Vq2a+p4bn5WcENexhSoxQOagxHC46tiq1bZ1q7VVq3nboMQACYG4HvrJ9W2nzcpTYBMR6WUKcNL/7dzgjXlrrIYLn4ThU/2uSKRXdDbNO9DpH93N47PadhNsbdXqtHtgoxe8oo7h2sfxWSmJ4SCV4LVaNY/PihbAmlutdteqlZIQ0DXEREQk5inASf9W/rnXbbK+Ei58AkZM97sikV7zq2c/ijrN+zWPrOb+tz/vnvFZ4a5+kaEqc1AiIzMjw1WAQeFWrd4anyUiItJfKcBJ/1W+MRzeKrzwNnKm3xWJ9Ji6xiDvb9rF6uJyVm0sZ3VxBZsr6qLuW98UwgG56cnh0BQlWLUbizWoXatWSmKApHgFLRERkd6mACf9U0UJ3H0y1JbDhY/ByFl+VyTSbZqCIdZurfLCWnEFqzaWs3ZrZUvrWU56EjPyMymtqqeyrqnD8XmZKTz4rcN7u2wRERHpBgpw0v/s2uSFt5odsOgxyDvY74pE9pmZUVRWE25Zq2B1cTnvbaqgrtEbU5aeHM+M/EwumTeO6fmZzBiVwfDByTjnOp3m/eoTJvn1ckRERGQ/KcBJ/7JrszdhSdU2L7zld7h0hkiftqWijlXF5awu9rpBrtpYzq5wK1pSfBxT8zI4d85oZuRnMj0/g4KsVOI6mZhD07yLiIj0Pwpw0n9UbvHC267NsOhRGDXH74pEdqu8poHVxRUtXSFXF5ezdVc94E0QMmlYOgumj2B6OKxNHJa+19PVa5p3ERGR/kUBTvqHqm3h8LYJLngYRh/md0UibdQ2BHlvU0XLBCOri8spKqtp2T4uO5XDx2W1dIM8cEQGKYkBHysWERGRvkgBTmJfVakX3iqK4fy/w5gj/K5IBrjGYIiPt1R6XSE3VrCq2JtkpHmG/uGDk5kxKoMzZ49iRn4m0/IzyEhJ8LdoERERiQkKcBLbqrfD4lO9i3Wf/3comOt3RTLAhELGuu3VrWPWist5f9OulgtXZ6QkMGNUJscdOMxrXcvPIHdwss9Vi4iISKxSgJPYVV0GixfCjnVw3oMw9ii/K5J+zszYVFHH6o2tY9bWFFdQWe9NMpKSEGBq3mAuPGwM00d5YW300EG6VpqIiIh0GwU4iU01O+CehbD9EzjvARj3Bb8rkn5oR3VDSzfI5olGtld5k4zExzkmj0jn1JkjvRkhR2UwPieN+L2cZERERERkbyjASeyp2eG1vJWuhXPvh8Jj/a5I+oHq+ibeK/G6QDa3rm3cUQuAc94kI/MmZrdM33/AiMEkJ2iSEREREeldCnASW2p3wj1fhtKP4Jz7YfwX/a5IYlBDU4iPtuxiVURXyE+3VbVMMpKXmcL0/AzOP3QM0/MzmJaXQXqyJhkRERER/ynASeyoLYd7vgJb34ez74UJx/ldkcSAYMhYV1rFyojp+z/cXElD0JtkZGhqItPzMzhp6ghmjMpgen4m2WlJPlctIiIiEp0CnMSGul1w7+mwZQ2cfQ9MOtHviqQPMjOKd9a2zAa5amM575VUUN0QBCA1McDUvAwuOrKgpStk/pAUTTIiIiIiMUMBTvq++kovvG1eCWcthkkn+V2R9BHbq+pZXVzOyvAkI6uLK9hR3QBAYiCOA0ak85WD8pkRnhFyXE4agTiFNREREYldCnDSt9VXwr1nQMkKOPMumLzA74rEJ5V1jawpqWBVRFgrKW+dZGRCbhrHTs5tCWuThqeTFK9JRkRERKR/UYCTvqu+Cu47C4qXwRl3woGn+l2R9JK6xiAfbt7ldYXcWM6q4nLWbf//7d15eFX1gf/x9zchQNgS2SEBBNlRFkG06rjUDRcUV9y1dXR06tipU0c7+rODY0dbra0+Wpe6VNytVSuK4q51BxdQwo4sYUeWsIYs398fN2WQolwg4SQ379fz5Mm9595zzufKMU8+Oef7PeuIVZOMdGqZy8DO+VxwYBcGFOazd0EeTRv540ySJGU+f+NR7bRpHTw+EuZ/BKfeD/1GJJ1INaSiMjJj6RomzV/NF8WrmFS8immL11BWkWprrZs1YkBhHicOKNg8yUjLpg0TTi1JkpQMC5xqn03rU+Vt3gdwyh9h71OTTqRqEmNk3or1qan7q86sfbWghA1lqUlGmjdqwD6FeVx0cDcGVpW1DnmNnWREkiSpigVOtUvZBnjiTJjzHpx8L+xzWtKJtAuWlmzcfJ+1L+av4ssFq1m1vgyAhg2y6NexBSP360T/wjwGdMqna6umZDnJiCRJ0neywKn2KNsAT5wFX78LI+6GASOTTqQdsHpDGV9WTd8/qXgVE+evZnHJRgCyAvRs15xj+ranf6c8BhTm06t9c3KysxJOLUmSVLdY4FQ7lG2EJ8+B2W/DSXfBwLOSTqTvsbGsgskL/29GyInFq/l6+brNr+/ZqglDu7bcfGatX8cWNGnojxtJkqRd5W9USl55KTx9Hsx6A068Ewadk3QibaGsopLpS9YwqepSyInzVzNtyRoqKlOTjLRr0Yj+hfmcum8B/atujp3fxElGJEmSaoIFTskqL4Wnz4cZr8Lw22Hf85JOVK9VVkbmfLMuNX1/1b3WvlqwmtLySgBaNG7AgE75XNq7G/0L8xlQmE/7vMYJp5YkSao/LHBKTvkm+POFMP0VOP42GHxh0onqlRgji0s2fuvG2JOKV1GysRyAxjlZ7N0xj3P278KAqnFrXVo1cUZISZKkBFnglIyKMnjmRzBtLBx3K+x3UdKJMt6q9Zu2mL4/VdaWrikFoEFWoFf75hzfvyMDClPT9/ds14wGTjIiSZJUq1jgtPv9vbxNfRGO/Q0MvTjpRBln/aZyvlpQsnmCkUnFq5j7zfrNr3dr05SDuremf1VZ69exBY1zshNMLEmSpHRY4LR7VZTDX/4ZpoyBYTfD/v+SdKI6b1N5JdMWr9k8ff+k4tVMX7KGqjlG6JjXmP6F+YzcrxMDCvPZuyCPvNycZENLkiRpp1jgtPtUlMOzF0PR83D0r+CAy5JOVOdUVkZmL1/7ren7ixaVsKlqkpH8JjkMKMzn6L7tUjNCdsqjbXMnGZEkScoUaRW4EMIw4HYgG7g/xnjzVq+fA1xd9XQtcFmMcWI666qeqKyA5y+Fyc/CUTfAgZcnnajWeP7zBdwybhoLV22gY34uVx3TixGDCogxsnD1RibOX5U6uzY/NSPkmtLUJCNNGmazd0EeF/ygy+YZITu1zHWSEUmSpAwWYozf/4YQsoHpwFFAMTAeOCvGWLTFew4EpsQYV4YQjgX+O8a4fzrrbsuQIUPihAkTduFjqVaprIDnL4NJT8ERv4R/ujLpRLXG858v4BfPfsmGsorNyxpkBXq2a8bSNaUsX7sJgJzsQJ8OLTaPWRtQmE/3ts3IzrKsSZIkZaIQwqcxxiFbL0/nDNxQYGaMcXbVhp4ETgI2l7AY4wdbvP8joDDddZXhKivgrz9JlbcfXmd528ot46Z9q7wBlFdGpi9Zy0kDCxjQKVXY+nRoTqMGTjIiSZJU36VT4AqA+Vs8Lwb2/573XwS8vJPrKpNUVsIL/wYTn4DD/gsOuSrpRLXK7GVrWbBqwzZfq6iM/PaMAbs5kSRJkmq7dArctq7R2uZ1lyGEw0kVuIN3Yt1LgEsAOnfunEYs1WqVlTDmCvjiMTj0Gjjs6u2vU0+UbCzjzjdn8tD7XxPY9v8QHfNzd3csSZIk1QHpFLhioNMWzwuBhVu/KYTQH7gfODbG+M2OrAsQY7wPuA9SY+DSyKXaqrISXvoZfP5I6qzbYdcknahWqKiMPPPpfG4ZN41v1m3i9MGF7F3QgpvGfvsyytycbK46pleCSSVJklRbpVPgxgM9QghdgQXAmcDZW74hhNAZeBY4L8Y4fUfWVYaJEcb+HD79Exx8JRx+LTgrIuPnrGDUmMl8taCEwV324MEL96N/YT4ALRo33OYslJIkSdLWtlvgYozlIYTLgXGkbgXwYIxxcgjh0qrX7wGuB1oBf6iawrw8xjjku9atoc+ipMUIY6+CCQ/AQT+FI66v9+Vt4aoN3PTyVMZMXEiHvMbcfuZAThzQ8VtT/Y8YVGBhkyRJUlq2exuBJHgbgTooRnjlGvj4HvjB5XD0jfW6vG3YVMF9787m7ndmEiP8y6F7cemh3WjSMK1bL0qSJKme25XbCEjfL0YY91+p8nbAv9br8hZj5KUvF3HT2KksWLWB4/fpwDXH9qZTyyZJR5MkSVIGsMBp18QIr14HH/0B9r8UjvnfelvevlqwmhvGFPHJnBX06dCC354xgAO6tUo6liRJkjKIBU47L0Z4/Zfw4Z2w38Uw7OZ6Wd6Wry3lt69O48nx89mjSUP+9+R9GLlfJ7Kz6t9/C0mSJNUsC5x2Tozwxg3w/u0w5CI47pZ6V942lVcy+sM53P76DDaUVfDjg7pyxRE9yMvNSTqaJEmSMpQFTjsuRnjrV/DebTD4Qjju1npX3t6atpT/ebGI2cvWcWjPNvy/E/rSvW2zpGNJkiQpw1ngtOPevhnevQX2PR+O/x1kZSWdaLeZtWwtN75YxFvTltG1dVMevHAIh/dq+63bAkiSJEk1xQKnHfPOb+Cdm2HguXDC7fWmvJVsLOOO12fwpw/mkJuTzbXH9eGCA/ekYYP68fklSZJUO1jglL53b0ldOjngLDjxjnpR3ioqI09PmM+t46axYv0mRg7pxH8c3Ys2zRslHU2SJEn1kAVO6fnbbfDmjdB/JJx0F2RlJ52oxn3y9QpGjZnM5IUl7LfnHjw8fCh7F+QlHUuSJEn1mAVO2/f+7fDGKNjndBhxd8aXtwWrNnDT2Cm8OGkRHfIac8dZgxjev4Pj3CRJkpQ4C5y+3wd3wmvXw96nwoh7Mrq8bdhUwT3vzOLed2cRI/z0iB5ceuhe5DbM3M8sSZKkusUCp+/24R/g1Wuh7wg4+T7IzszDJcbImEmLuHnsFBau3sgJ/Tvwi+P6UJCfm3Q0SZIk6Vsy8zdy7bqP74Vxv4A+J8Kp92dseftqwWpGjZnM+Dkr6duhBb8/cxBDu7ZMOpYkSZK0TZn5W7l2zSd/hJf/E3qfAKc9CNk5SSeqdsvXlnLruGk8NWE+LZs05KZT9uGMIZ3IznKcmyRJkmovC5y+bfwDMPbn0Os4OO2hjCtvm8orefiDOdzxxgw2lFVw0UFd+bcjepCXm1mfU5IkSZnJAqf/M+EheOlK6DkMTv8TNGiYdKJq9dbUpfzPi0XMXr6Ow3u14boT+rJXm2ZJx5IkSZLSZoFTymej4cV/hx5HwxmjoUHm3Kh65tK13PhSEW9PW0a31k156ML9OLx326RjSZIkSTvMAif4/DF44QrofiSc8UjGlLfVG8q4440ZPPzBHHJzsrnu+D6c/4M9adggK+lokiRJ0k6xwNV3XzwBf/0JdDsMRj4GOY2TTrTLKiojT42fz62vTmPl+k2cuV8n/uPoXrRulhnFVJIkSfWXBa4+m/Q0PH8ZdD0EznoiI8rbx7O/YdSYIooWlTB0z5ZcP7wvexfkJR1LkiRJqhYWuPrqy2fguX+BPQ+Gs56EnLp90+rileu56eWpvDRpER3zGnPn2YM4fp8OhOBtASRJkpQ5LHD10Vd/gWcvhs4HwtlPQcMmSSfaaes3lXPPO7O5951ZhAA/O7InlxzSjdyG2UlHkyRJkqqdBa6+mfwc/OVi6HRAVXlrmnSinRJj5IWJC7n55aksWr2R4QM6cs2xvSnIr9tnEiVJkqTvY4GrT4pegGcugsL94Jw/Q6O6eQ+0L4tXM2rMZCbMXUm/ji24/cxBDO3aMulYkiRJUo2zwNUXU16EZ34EBYPh3GfqZHlbtqaUW8dN4+lP59OqaUN+feo+nDa4E9lZjnOTJElS/WCBqw+mvQx/vhA6DIRz/wKNmiedaIdsKq/kTx98zR1vzGIR3f4AABMTSURBVKS0vIKL/6kbl/+wOy0a5yQdTZIkSdqtLHCZbvo4eOo8aL8PnPcsNG6RdKK0xRh5c+pSbnxpCl8vX8cPe7fluuP70K1N3Tt7KEmSJFUHC1wmm/EaPHUutOsH5z0HjevO/dBmLl3DDS9O4d3py+jWpikP/Wg/Du/VNulYkiRJUqIscJlq5uvw5DnQpneqvOXmJ50oLavXl/H7N6Yz+sO5NGmYzf87oS/n/6ALOdlZSUeTJEmSEmeBy0Sz3oQnzoY2PeH8v0KT2j9DY0Vl5Mnx8/jtq9NZuX4TZ+7XmZ8f3ZNWzRolHU2SJEmqNSxwmWb22/DEWdC6B5z/Qp0obx/O+oYbXixiyqIShnZtyS+H96Vfx7pzuackSZK0u1jgMsnX78LjZ0LLbnXizNv8Feu56eUpjP1yMQX5udx19r4ct097QvC2AJIkSdK2WOAyxZz34PGRsEeX1Jm3pq2TTvSd1m8q5+63Z3Hfu7MJAa48qieXHNKNxjnZSUeTJEmSajULXCaY+wE8dgbkdYILxkCzNkkn2qYYIy9MXMhNY6eyuGQjJw3syNXDetMxPzfpaJIkSVKdkFaBCyEMA24HsoH7Y4w3b/V6b+AhYF/g2hjjrVu8NgdYA1QA5THGIdUTXQDM+wgeOx1adKwqb7Vzqv1JxasYNaaIT+euZO+CFtx59iCG7Fm7L/GUJEmSapvtFrgQQjZwF3AUUAyMDyG8EGMs2uJtK4ArgBHfsZnDY4zLdzWstjL/E3j0NGjWLlXemrdLOtE/WLpmI7e8Mo1nPiumVdOG/ObU/pw2uJCsLMe5SZIkSTsqnTNwQ4GZMcbZACGEJ4GTgM0FLsa4FFgaQji+RlLqHxVPgEdOSV0ueeGL0KJD0om+pbS8gofen8Odb86ktLyCS/6pG5f/sDvNG+ckHU2SJEmqs9IpcAXA/C2eFwP778A+IvBqCCEC98YY79vWm0IIlwCXAHTu3HkHNl8PLfgUHjk5NVHJBS+mLp+sJWKMvDFlKTe+VMScb9ZzZJ+2XHt8X7q2bpp0NEmSJKnOS6fAbetat7gD+zgoxrgwhNAWeC2EMDXG+O4/bDBV7O4DGDJkyI5sv35Z+HmqvOXukTrzlleQdKLNZixZww0vFvG3Gcvp3rYZD/94KIf2rJ0TqkiSJEl1UToFrhjotMXzQmBhujuIMS6s+r40hPAcqUsy/6HAKQ2LJsLoEdA4r6q8FSadCIDV68v43evTeeSjuTRpmM31J/TlvB90ISc7K+lokiRJUkZJp8CNB3qEELoCC4AzgbPT2XgIoSmQFWNcU/X4aOCGnQ1bry3+EkafBI2apy6bzE/+MtPyikqeGD+f216dxuoNZZw1tDNXHtWTVs0aJR1NkiRJykjbLXAxxvIQwuXAOFK3EXgwxjg5hHBp1ev3hBDaAxOAFkBlCOHfgb5Aa+C5EMLf9/V4jPGVmvkoGWzxV/DwiZDTNDXb5B5dkk7EB7OWc8OYIqYuXsP+XVvyy+H96NuxRdKxJEmSpIyW1n3gYoxjgbFbLbtni8eLSV1aubUSYMCuBKz3lhTB6BOhQWO4cAy07JponPkr1vO/Y6fw8leLKcjP5e5z9mXY3u2pKumSJEmSalBaBU4JWToFHh4O2Q1TY95adkssyrrScu5+exb3/W022SHwH0f15OJDutE4JzuxTJIkSVJ9Y4GrrZZNS5W3rAapMW+t9kokRoyR579YwM0vT2VJSSkjBnbk6mN70yEvN5E8kiRJUn1mgauNls9IlTdCasxb6+6JxJg4fxWjxkzms3mr6F+Yxx/O2ZfBXVomkkWSJEmSBa72WT4T/nQCxEq48CVo03O3R1haspHfjJvGM58W07pZI245rT+n7ltIVpbj3CRJkqQkWeBqk29mwcMnQGV5asxbm167dfel5RU8+N4c7nxzBmUVkUsP3YufHL4XzRvn7NYckiRJkrbNAldbrJidumyyYlPqssm2fXbbrmOMvFa0hF+NncLcb9ZzZJ92XHd8H/Zs3XS3ZZAkSZK0fRa42mDF1/Cn4VC2AS54Adr12227nr5kDTeMKeK9mcvp0bYZo388lEN6ttlt+5ckSZKUPgtc0lbOTZ15K1sH578A7ffZLbtdtX4Tv3ttOo9+PI+mDbP57+F9OeeALuRkZ+2W/UuSJEnacRa4JK2alxrzVlqSKm8d+tf4LssrKnnik3n89rXplGwo4+z9O3PlUb1o2bRhje9bkiRJ0q6xwCVldXFqtskNq+GCv0LHgTW+yw9mLmfUmCKmLVnDD7q14vrhfenToUWN71eSJElS9bDAJWH1gqrythLOfx46DqrR3c37Zj2/GlvEuMlLKNwjl3vO3Zdj+rUnBG8LIEmSJNUlFrjdrWRRaszbuuWp8lYwuMZ2ta60nD+8PZM//u1rGmQFrjqmFxcd3JXGOdk1tk9JkiRJNccCtzutWZwa87Z2CZz7LBQOqZHdVFZGnv9iATe/PJWla0o5ZVAB/zmsN+3zGtfI/iRJkiTtHha43WXNktRlkyWL4LxnofP+NbKbz+etZNSYIr6Yv4oBhXncfe5gBnfZo0b2JUmSJGn3ssDtDmuXpi6bLFkI5z4DnQ+o9l0sKdnIr1+ZyrOfLaBN80bcevoAThlUQFaW49wkSZKkTGGBq2lrl8HDJ8Lq+XDOn6HLgdW6+Y1lFTzw3tfc9dZMyisilx22Fz85vDvNGvlPK0mSJGUaf8uvSeu+gdEnwco5cM7TsOfB1bbpGCOvFi3hVy9NYd6K9Rzdtx3XHt+HLq2aVts+JEmSJNUuFriasn4FjD4RVsyCs5+CrodU26anLV7DDS9O5v2Z39CzXTMevWh/Du7Rutq2L0mSJKl2ssDVhL+Xt+Uz4KwnoNth1bLZVes3cdtr03n0o7k0b5zDqBP7cc7+nWmQnVUt25ckSZJUu1ngqtuGlfDICFg2Dc58ArofscubLK+o5PFP5nHba9Mp2VDGuQd04WdH9mSPpg2rIbAkSZKkusICV502rILRI2DpFBj5GPQ4cpc3+f7M5YwaM5npS9Zy4F6tuH54X3q3b1ENYSVJkiTVNRa46rJxNTxyMiyZDCMfhZ5H79Lm5n6zjl+9NIVXi5bQqWUu9543mKP7tiMEbwsgSZIk1VcWuOqwsQQePRUWfwlnjIZew3Z6U2tLy7nrrZk88LevaZAduOqYXlx0cFca52RXY2BJkiRJdZEFbleVroHHToOFn8PpD0Pv43ZqM5WVkec+X8CvX5nK0jWlnLJvAVcP6027Fo2rObAkSZKkusoCtytK18Jjp0PxBDj9Iehzwk5t5rN5Kxk1poiJ81cxoFM+9543mEGd96jmsJIkSZLqOgvcztq0Dh4/A+Z/Aqc9AH1P2uFNLCnZyK9fnsqzny+gbfNG3HbGAEYMLCAry3FukiRJkv6RBW5nbFoHj4+EeR/CqfdDv5N3aPWNZRU88N7X3PXWTMorIv962F786+HdadbIfw5JkiRJ383GsKM2rU+Vt7nvwyl/hL1PTXvVGCPjJi/mxpemULxyA8f0a8e1x/Wlc6smNRhYkiRJUqawwKVj0tPwxg2wuhgaNITyUjj5PtjntLQ3MXVxCTeMKeKDWd/Qq11zHvvn/Tmoe+saDC1JkiQp01jgtmfS0zDmCijbkHpeXgrZDSHN+7GtXLeJ216bzmMfz6VFbg7/c1I/zhramQbZWTUYWpIkSVImssBtzxs3/F95+7uKTanl/c/4ztXKKip57KO5/O71GawtLee8A7rws6N6kt+kYQ0HliRJkpSpLHDbs7p4x5YDf5uxjBvGFDFj6VoO6t6K60/oR6/2zWsooCRJkqT6wgK3PXmFsHr+tpdvZc7yddz40hRen7KEzi2bcN95gzmqbztCmpdbSpIkSdL3scBtzxHXf3sMHEBObmp5lbWl5dz55kwefO9rcrIDVw/rzY8P3pNGDbITCCxJkiQpU6U1k0YIYVgIYVoIYWYI4ZptvN47hPBhCKE0hPDzHVm31ut/Bgy/A/I6ASH1ffgd0P8MKisjf54wn8NvfZt73pnFiQM78tbPD+Oyw/ayvEmSJEmqdts9AxdCyAbuAo4CioHxIYQXYoxFW7xtBXAFMGIn1q39+p/xDxOWfDp3JaPGTGZS8WoGdsrnj+cPYWCn/IQCSpIkSaoP0rmEcigwM8Y4GyCE8CRwErC5hMUYlwJLQwjH7+i6dcHzny/glnHTWLhqA+1aNKZwj8ZMmLuKdi0a8buRAzhpQAFZWY5zkyRJklSz0ilwBcCWs3gUA/unuf201w0hXAJcAtC5c+c0N1/znv98Ab949ks2lFUAsLhkI4tLNnJUn7b8/sxBNG3kMEJJkiRJu0c6Y+C2dWopprn9tNeNMd4XYxwSYxzSpk2bNDdf824ZN21zedtS0aI1ljdJkiRJu1U6Ba4Y6LTF80JgYZrb35V1a4WFqzbs0HJJkiRJqinpFLjxQI8QQtcQQkPgTOCFNLe/K+vWCh3zc3douSRJkiTVlO0WuBhjOXA5MA6YAjwdY5wcQrg0hHApQAihfQihGLgSuC6EUBxCaPFd69bUh6kJVx3Ti9ycb98SIDcnm6uO6ZVQIkmSJEn1VYgx3eFsu8+QIUPihAkTko6x2ZazUHbMz+WqY3oxYlBB0rEkSZIkZagQwqcxxiFbL3cWjjSMGFRgYZMkSZKUuHTGwEmSJEmSagELnCRJkiTVERY4SZIkSaojLHCSJEmSVEdY4CRJkiSpjrDASZIkSVIdUSvvAxdCWAbMTTrHNrQGlicdQhnL40s1yeNLNcnjSzXJ40s1rbYeY11ijG22XlgrC1xtFUKYsK2b6UnVweNLNcnjSzXJ40s1yeNLNa2uHWNeQilJkiRJdYQFTpIkSZLqCAvcjrkv6QDKaB5fqkkeX6pJHl+qSR5fqml16hhzDJwkSZIk1RGegZMkSZKkOsICJ0mSJEl1hAUuDSGEYSGEaSGEmSGEa5LOo8wSQngwhLA0hPBV0lmUeUIInUIIb4UQpoQQJocQfpp0JmWOEELjEMInIYSJVcfXqKQzKfOEELJDCJ+HEF5MOosySwhhTgjhyxDCFyGECUnnSZdj4LYjhJANTAeOAoqB8cBZMcaiRIMpY4QQDgHWAqNjjHsnnUeZJYTQAegQY/wshNAc+BQY4c8wVYcQQgCaxhjXhhBygPeAn8YYP0o4mjJICOFKYAjQIsZ4QtJ5lDlCCHOAITHG2ngT7+/kGbjtGwrMjDHOjjFuAp4ETko4kzJIjPFdYEXSOZSZYoyLYoyfVT1eA0wBCpJNpUwRU9ZWPc2p+vIvw6o2IYRC4Hjg/qSzSLWFBW77CoD5Wzwvxl9+JNVBIYQ9gUHAx8kmUSapurztC2Ap8FqM0eNL1en3wH8ClUkHUUaKwKshhE9DCJckHSZdFrjtC9tY5l8XJdUpIYRmwF+Af48xliSdR5kjxlgRYxwIFAJDQwheCq5qEUI4AVgaY/w06SzKWAfFGPcFjgV+UjWspdazwG1fMdBpi+eFwMKEskjSDqsam/QX4LEY47NJ51FmijGuAt4GhiUcRZnjIODEqnFKTwI/DCE8mmwkZZIY48Kq70uB50gNnar1LHDbNx7oEULoGkJoCJwJvJBwJklKS9UkEw8AU2KMtyWdR5klhNAmhJBf9TgXOBKYmmwqZYoY4y9ijIUxxj1J/f71Zozx3IRjKUOEEJpWTe5FCKEpcDRQJ2YEt8BtR4yxHLgcGEdq8P/TMcbJyaZSJgkhPAF8CPQKIRSHEC5KOpMyykHAeaT+cv1F1ddxSYdSxugAvBVCmETqD56vxRid6l1SXdAOeC+EMBH4BHgpxvhKwpnS4m0EJEmSJKmO8AycJEmSJNURFjhJkiRJqiMscJIkSZJUR1jgJEmSJKmOsMBJkiRJUh1hgZMkZawQQsUWt0/4IoRwTTVue88QQp24Z5AkKXM0SDqAJEk1aEOMcWDSISRJqi6egZMk1TshhDkhhF+HED6p+upetbxLCOGNEMKkqu+dq5a3CyE8F0KYWPV1YNWmskMIfwwhTA4hvBpCyE3sQ0mS6gULnCQpk+VudQnlyC1eK4kxDgXuBH5ftexOYHSMsT/wGHBH1fI7gHdijAOAfYHJVct7AHfFGPsBq4BTa/jzSJLquRBjTDqDJEk1IoSwNsbYbBvL5wA/jDHODiHkAItjjK1CCMuBDjHGsqrli2KMrUMIy4DCGGPpFtvYE3gtxtij6vnVQE6M8caa/2SSpPrKM3CSpPoqfsfj73rPtpRu8bgCx5ZLkmqYBU6SVF+N3OL7h1WPPwDOrHp8DvBe1eM3gMsAQgjZIYQWuyukJElb8i+FkqRMlhtC+GKL56/EGP9+K4FGIYSPSf0x86yqZVcAD4YQrgKWAT+qWv5T4L4QwkWkzrRdBiyq8fSSJG3FMXCSpHqnagzckBjj8qSzSJK0I7yEUpIkSZLqCM/ASZIkSVId4Rk4SZIkSaojLHCSJEmSVEdY4CRJkiSpjrDASZIkSVIdYYGTJEmSpDri/wPnGp1bGMi0qQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_train = 4000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "solvers = {}\n",
    "\n",
    "for update_rule in ['sgd', 'sgd_momentum']:\n",
    "  print('running with ', update_rule)\n",
    "  model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2)\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=5, batch_size=100,\n",
    "                  update_rule=update_rule,\n",
    "                  optim_config={\n",
    "                    'learning_rate': 5e-3,\n",
    "                  },\n",
    "                  verbose=True)\n",
    "  solvers[update_rule] = solver\n",
    "  solver.train()\n",
    "  print()\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "for update_rule, solver in solvers.items():\n",
    "  plt.subplot(3, 1, 1)\n",
    "  plt.plot(solver.loss_history, 'o', label=\"loss_%s\" % update_rule)\n",
    "  \n",
    "  plt.subplot(3, 1, 2)\n",
    "  plt.plot(solver.train_acc_history, '-o', label=\"train_acc_%s\" % update_rule)\n",
    "\n",
    "  plt.subplot(3, 1, 3)\n",
    "  plt.plot(solver.val_acc_history, '-o', label=\"val_acc_%s\" % update_rule)\n",
    "  \n",
    "for i in [1, 2, 3]:\n",
    "  plt.subplot(3, 1, i)\n",
    "  plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RMSProp and Adam\n",
    "RMSProp [1] and Adam [2] are update rules that set per-parameter learning rates by using a running average of the second moments of gradients.\n",
    "\n",
    "In the file `cs231n/optim.py`, implement the RMSProp update rule in the `rmsprop` function and implement the Adam update rule in the `adam` function, and check your implementations using the tests below.\n",
    "\n",
    "**NOTE:** Please implement the _complete_ Adam update rule (with the bias correction mechanism), not the first simplified version mentioned in the course notes. \n",
    "\n",
    "[1] Tijmen Tieleman and Geoffrey Hinton. \"Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude.\" COURSERA: Neural Networks for Machine Learning 4 (2012).\n",
    "\n",
    "[2] Diederik Kingma and Jimmy Ba, \"Adam: A Method for Stochastic Optimization\", ICLR 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  9.524687511038133e-08\n",
      "cache error:  2.6477955807156126e-09\n"
     ]
    }
   ],
   "source": [
    "# Test RMSProp implementation\n",
    "from cs231n.optim import rmsprop\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "cache = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-2, 'cache': cache}\n",
    "next_w, _ = rmsprop(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [-0.39223849, -0.34037513, -0.28849239, -0.23659121, -0.18467247],\n",
    "  [-0.132737,   -0.08078555, -0.02881884,  0.02316247,  0.07515774],\n",
    "  [ 0.12716641,  0.17918792,  0.23122175,  0.28326742,  0.33532447],\n",
    "  [ 0.38739248,  0.43947102,  0.49155973,  0.54365823,  0.59576619]])\n",
    "expected_cache = np.asarray([\n",
    "  [ 0.5976,      0.6126277,   0.6277108,   0.64284931,  0.65804321],\n",
    "  [ 0.67329252,  0.68859723,  0.70395734,  0.71937285,  0.73484377],\n",
    "  [ 0.75037008,  0.7659518,   0.78158892,  0.79728144,  0.81302936],\n",
    "  [ 0.82883269,  0.84469141,  0.86060554,  0.87657507,  0.8926    ]])\n",
    "\n",
    "# You should see relative errors around e-7 or less\n",
    "print('next_w error: ', rel_error(expected_next_w, next_w))\n",
    "print('cache error: ', rel_error(expected_cache, config['cache']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  1.1395691798535431e-07\n",
      "v error:  4.208314038113071e-09\n",
      "m error:  4.214963193114416e-09\n"
     ]
    }
   ],
   "source": [
    "# Test Adam implementation\n",
    "from cs231n.optim import adam\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "m = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "v = np.linspace(0.7, 0.5, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-2, 'm': m, 'v': v, 't': 5}\n",
    "next_w, _ = adam(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [-0.40094747, -0.34836187, -0.29577703, -0.24319299, -0.19060977],\n",
    "  [-0.1380274,  -0.08544591, -0.03286534,  0.01971428,  0.0722929],\n",
    "  [ 0.1248705,   0.17744702,  0.23002243,  0.28259667,  0.33516969],\n",
    "  [ 0.38774145,  0.44031188,  0.49288093,  0.54544852,  0.59801459]])\n",
    "expected_v = np.asarray([\n",
    "  [ 0.69966,     0.68908382,  0.67851319,  0.66794809,  0.65738853,],\n",
    "  [ 0.64683452,  0.63628604,  0.6257431,   0.61520571,  0.60467385,],\n",
    "  [ 0.59414753,  0.58362676,  0.57311152,  0.56260183,  0.55209767,],\n",
    "  [ 0.54159906,  0.53110598,  0.52061845,  0.51013645,  0.49966,   ]])\n",
    "expected_m = np.asarray([\n",
    "  [ 0.48,        0.49947368,  0.51894737,  0.53842105,  0.55789474],\n",
    "  [ 0.57736842,  0.59684211,  0.61631579,  0.63578947,  0.65526316],\n",
    "  [ 0.67473684,  0.69421053,  0.71368421,  0.73315789,  0.75263158],\n",
    "  [ 0.77210526,  0.79157895,  0.81105263,  0.83052632,  0.85      ]])\n",
    "\n",
    "# You should see relative errors around e-7 or less\n",
    "print('next_w error: ', rel_error(expected_next_w, next_w))\n",
    "print('v error: ', rel_error(expected_v, config['v']))\n",
    "print('m error: ', rel_error(expected_m, config['m']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have debugged your RMSProp and Adam implementations, run the following to train a pair of deep networks using these new update rules:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running with  adam\n",
      "(Iteration 1 / 200) loss: 2.695519\n",
      "(Epoch 0 / 5) train acc: 0.167000; val_acc: 0.142000\n",
      "(Iteration 11 / 200) loss: 2.145033\n",
      "(Iteration 21 / 200) loss: 1.895800\n",
      "(Iteration 31 / 200) loss: 1.889680\n",
      "(Epoch 1 / 5) train acc: 0.411000; val_acc: 0.344000\n",
      "(Iteration 41 / 200) loss: 1.733051\n",
      "(Iteration 51 / 200) loss: 1.769120\n",
      "(Iteration 61 / 200) loss: 1.768141\n",
      "(Iteration 71 / 200) loss: 1.637140\n",
      "(Epoch 2 / 5) train acc: 0.450000; val_acc: 0.359000\n",
      "(Iteration 81 / 200) loss: 1.564389\n",
      "(Iteration 91 / 200) loss: 1.424076\n",
      "(Iteration 101 / 200) loss: 1.375047\n",
      "(Iteration 111 / 200) loss: 1.349079\n",
      "(Epoch 3 / 5) train acc: 0.460000; val_acc: 0.335000\n",
      "(Iteration 121 / 200) loss: 1.533449\n",
      "(Iteration 131 / 200) loss: 1.635726\n",
      "(Iteration 141 / 200) loss: 1.346661\n",
      "(Iteration 151 / 200) loss: 1.340596\n",
      "(Epoch 4 / 5) train acc: 0.552000; val_acc: 0.352000\n",
      "(Iteration 161 / 200) loss: 1.307821\n",
      "(Iteration 171 / 200) loss: 1.313640\n",
      "(Iteration 181 / 200) loss: 1.342309\n",
      "(Iteration 191 / 200) loss: 1.198045\n",
      "(Epoch 5 / 5) train acc: 0.589000; val_acc: 0.357000\n",
      "\n",
      "running with  rmsprop\n",
      "(Iteration 1 / 200) loss: 2.542284\n",
      "(Epoch 0 / 5) train acc: 0.122000; val_acc: 0.153000\n",
      "(Iteration 11 / 200) loss: 1.986341\n",
      "(Iteration 21 / 200) loss: 2.018241\n",
      "(Iteration 31 / 200) loss: 1.847864\n",
      "(Epoch 1 / 5) train acc: 0.362000; val_acc: 0.305000\n",
      "(Iteration 41 / 200) loss: 1.688328\n",
      "(Iteration 51 / 200) loss: 1.734579\n",
      "(Iteration 61 / 200) loss: 1.716976\n",
      "(Iteration 71 / 200) loss: 1.659823\n",
      "(Epoch 2 / 5) train acc: 0.443000; val_acc: 0.334000\n",
      "(Iteration 81 / 200) loss: 1.550959\n",
      "(Iteration 91 / 200) loss: 1.763687\n",
      "(Iteration 101 / 200) loss: 1.629743\n",
      "(Iteration 111 / 200) loss: 1.639855\n",
      "(Epoch 3 / 5) train acc: 0.466000; val_acc: 0.357000\n",
      "(Iteration 121 / 200) loss: 1.649752\n",
      "(Iteration 131 / 200) loss: 1.536605\n",
      "(Iteration 141 / 200) loss: 1.703027\n",
      "(Iteration 151 / 200) loss: 1.437044\n",
      "(Epoch 4 / 5) train acc: 0.523000; val_acc: 0.356000\n",
      "(Iteration 161 / 200) loss: 1.318003\n",
      "(Iteration 171 / 200) loss: 1.566556\n",
      "(Iteration 181 / 200) loss: 1.186631\n",
      "(Iteration 191 / 200) loss: 1.454792\n",
      "(Epoch 5 / 5) train acc: 0.548000; val_acc: 0.360000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learning_rates = {'rmsprop': 1e-4, 'adam': 1e-3}\n",
    "for update_rule in ['adam', 'rmsprop']:\n",
    "  print('running with ', update_rule)\n",
    "  model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2)\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=5, batch_size=100,\n",
    "                  update_rule=update_rule,\n",
    "                  optim_config={\n",
    "                    'learning_rate': learning_rates[update_rule]\n",
    "                  },\n",
    "                  verbose=True)\n",
    "  solvers[update_rule] = solver\n",
    "  solver.train()\n",
    "  print()\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "for update_rule, solver in list(solvers.items()):\n",
    "  plt.subplot(3, 1, 1)\n",
    "  plt.plot(solver.loss_history, 'o', label=update_rule)\n",
    "  \n",
    "  plt.subplot(3, 1, 2)\n",
    "  plt.plot(solver.train_acc_history, '-o', label=update_rule)\n",
    "\n",
    "  plt.subplot(3, 1, 3)\n",
    "  plt.plot(solver.val_acc_history, '-o', label=update_rule)\n",
    "  \n",
    "for i in [1, 2, 3]:\n",
    "  plt.subplot(3, 1, i)\n",
    "  plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## Inline Question 3:\n",
    "\n",
    "AdaGrad, like Adam, is a per-parameter optimization method that uses the following update rule:\n",
    "\n",
    "```\n",
    "cache += dw**2\n",
    "w += - learning_rate * dw / (np.sqrt(cache) + eps)\n",
    "```\n",
    "\n",
    "John notices that when he was training a network with AdaGrad that the updates became very small, and that his network was learning slowly. Using your knowledge of the AdaGrad update rule, why do you think the updates would become very small? Would Adam have the same issue?\n",
    "\n",
    "\n",
    "## Answer: \n",
    "[FILL THIS IN]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train a good model!\n",
    "Train the best fully-connected model that you can on CIFAR-10, storing your best model in the `best_model` variable. We require you to get at least 50% accuracy on the validation set using a fully-connected net.\n",
    "\n",
    "If you are careful it should be possible to get accuracies above 55%, but we don't require it for this part and won't assign extra credit for doing so. Later in the assignment we will ask you to train the best convolutional network that you can on CIFAR-10, and we would prefer that you spend your effort working on convolutional nets rather than fully-connected nets.\n",
    "\n",
    "You might find it useful to complete the `BatchNormalization.ipynb` and `Dropout.ipynb` notebooks before completing this part, since those techniques can help you train powerful models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "best_model = None\n",
    "################################################################################\n",
    "# TODO: Train the best FullyConnectedNet that you can on CIFAR-10. You might   #\n",
    "# find batch/layer normalization and dropout useful. Store your best model in  #\n",
    "# the best_model variable.                                                     #\n",
    "################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "pass\n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test your model!\n",
    "Run your best model on the validation and test sets. You should achieve above 50% accuracy on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred = np.argmax(best_model.loss(data['X_test']), axis=1)\n",
    "y_val_pred = np.argmax(best_model.loss(data['X_val']), axis=1)\n",
    "print('Validation set accuracy: ', (y_val_pred == data['y_val']).mean())\n",
    "print('Test set accuracy: ', (y_test_pred == data['y_test']).mean())"
   ]
  }
 ],
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